#Face Recognition The system

[ausetkmt]

The first time Karl Ricanek was stopped by police for “driving while Black” was in the summer of 1995. He was twenty-five and had just qualified as an engineer and started work at the US Department of Defense’s Naval Undersea Warfare Center in Newport, Rhode Island, a wealthy town known for its spectacular cliff walks and millionaires’ mansions. That summer, he had bought his first nice car—a two-year-old dark green Infiniti J30T that cost him roughly $30,000 (US).

One evening, on his way back to the place he rented in First Beach, a police car pulled him over. Karl was polite, distant, knowing not to seem combative or aggressive. He knew, too, to keep his hands in visible places and what could happen if he didn’t. It was something he’d been trained to do from a young age.

The cop asked Karl his name, which he told him, even though he didn’t have to. He was well aware that if he wanted to get out of this thing, he had to cooperate. He felt at that moment he had been stripped of any rights, but he knew this was what he—and thousands of others like him—had to live with. This is a nice car, the cop told Karl. How do you afford a fancy car like this?

What do you mean? Karl thought furiously. None of your business how I afford this car. Instead, he said, “Well, I’m an engineer. I work over at the research centre. I bought the car with my wages.”

That wasn’t the last time Karl was pulled over by a cop. In fact, it wasn’t even the last time in Newport. And when friends and colleagues shrugged, telling him that getting stopped and being asked some questions didn’t sound like a big deal, he let it lie. But they had never been stopped simply for “driving while white”; they hadn’t been subjected to the humiliation of being questioned as law-abiding adults, purely based on their visual identity; they didn’t have to justify their presence and their choices to strangers and be afraid for their lives if they resisted.

Karl had never broken the law. He’d worked as hard as anybody else, doing all the things that bright young people were supposed to do in America. So why, he thought, can’t I just be left alone?

Karl grew up with four older siblings in Deanwood, a primarily Black neighbourhood in the northeastern corner of Washington, DC, with a white German father and a Black mother. When he left Washington, DC, at eighteen for college, he had a scholarship to study at North Carolina A&T State University, which graduates the largest numbers of Black engineers in the US. It was where Karl learned to address problems with technical solutions, rather than social ones. He taught himself to emphasize his academic credentials and underplay his background so he would be taken more seriously amongst peers.

After working in Newport, Karl went into academia, at the University of North Carolina, Wilmington. In particular, he was interested in teaching computers to identify faces even better than humans do. His goal seemed simple: first, unpick how humans see faces, and then teach computers how to do it more efficiently.

When he started out back in the ’80s and ’90s, Karl was developing AI technology to help the US Navy’s submarine fleet navigate autonomously. At the time, computer vision was a slow-moving field, in which machines were merely taught to recognize objects rather than people’s identities. The technology was nascent—and pretty terrible. The algorithms he designed were trying to get the machine to say: that’s a bottle, these are glasses, this is a table, these are humans. Each year, they made incremental, single-digit improvements in precision.

Then, a new type of AI known as deep learning emerged—the same discipline that allowed miscreants to generate sexually deviant deepfakes of Helen Mort and Noelle Martin, and the model that underpins ChatGPT. The cutting-edge technology was helped along by an embarrassment of data riches—in this case, millions of photos uploaded to the web that could be used to train new image recognition algorithms.

Deep learning catapulted the small gains Karl was seeing into real progress. All of a sudden, what used to be a 1 percent improvement was now 10 percent each year. It meant software could now be used not just to classify objects but to recognize unique faces.

When Karl first started working on the problem of facial recognition, it wasn’t supposed to be used live on protesters or pedestrians or ordinary people. It was supposed to be a photo analysis tool. From its inception in the ’90s, researchers knew there were biases and inaccuracies in how the algorithms worked. But they hadn’t quite figured out why.

The biometrics community viewed the problems as academic—an interesting computer-vision challenge affecting a prototype still in its infancy. They broadly agreed that the technology wasn’t ready for prime-time use, and they had no plans to profit from it.

As the technology steadily improved, Karl began to develop experimental AI analytics models to spot physical signs of illnesses like cardiovascular disease, Alzheimer’s, or Parkinson’s from a person’s face. For instance, a common symptom of Parkinson’s is frozen or stiff facial expressions, brought on by changes in the face’s muscles. AI technology could be used to analyse these micro muscular changes and detect the onset of disease early. He told me he imagined inventing a mirror that you could look at each morning that would tell you (or notify a trusted person) if you were developing symptoms of degenerative neurological disease. He founded a for-profit company, Lapetus Solutions, which predicted life expectancy through facial analytics, for the insurance market.

His systems were used by law enforcement to identify trafficked children and notorious criminal gangsters such as Whitey Bulger. He even looked into identifying faces of those who had changed genders, by testing his systems on videos of transsexual people undergoing hormonal transitions, an extremely controversial use of the technology. He became fixated on the mysteries locked up in the human face, regardless of any harms or negative consequences.

In the US, it was 9/11 that, quite literally overnight, ramped up the administration’s urgent need for surveillance technologies like face recognition, supercharging investment in and development of these systems. The issue was no longer merely academic, and within a few years, the US government had built vast databases containing the faces and other biometric data of millions of Iraqis, Afghans, and US tourists from around the world. They invested heavily in commercializing biometric research like Karl’s; he received military funding to improve facial recognition algorithms, working on systems to recognize obscured and masked faces, young faces, and faces as they aged. American domestic law enforcement adapted counterterrorism technology, including facial recognition, to police street crime, gang violence, and even civil rights protests.

It became harder for Karl to ignore what AI facial analytics was now being developed for. Yet, during those years, he resisted critique of the social impacts of the powerful technology he was helping create. He rarely sat on ethics or standards boards at his university, because he thought they were bureaucratic and time consuming. He described critics of facial recognition as “social justice warriors” who didn’t have practical experience of building this technology themselves. As far as he was concerned, he was creating tools to help save children and find terrorists, and everything else was just noise.

But it wasn’t that straightforward. Technology companies, both large and small, had access to far more face data and had a commercial imperative to push forward facial recognition. Corporate giants such as Meta and Chinese-owned TikTok, and start-ups like New York–based Clearview AI and Russia’s NTech Labs, own even larger databases of faces than many governments do—and certainly more than researchers like Karl do. And they’re all driven by the same incentive: making money.

These private actors soon uprooted systems from academic institutions like Karl’s and started selling immature facial recognition solutions to law enforcement, intelligence agencies, governments, and private entities around the world. In January 2020, the New York Times published a story about how Clearview AI had taken billions of photos from the web, including sites like LinkedIn and Instagram, to build powerful facial recognition capabilities bought by several police forces around the world.

The technology was being unleashed from Argentina to Alabama with a life of its own, blowing wild like gleeful dandelion seeds taking root at will. In Uganda, Hong Kong, and India, it has been used to stifle political opposition and civil protest. In the US, it was used to track Black Lives Matter protests and Capitol rioters during the uprising in January 2021, and in London to monitor revellers at the annual Afro-Caribbean carnival in Notting Hill.

And it’s not just a law enforcement tool: facial recognition is being used to catch pickpockets and petty thieves. It is deployed at the famous Gordon’s Wine Bar in London, scanning for known troublemakers. It’s even been used to identify dead Russian soldiers in Ukraine. The question whether it was ready for prime-time use has taken on an urgency as it impacts the lives of billions around the world.

Karl knew the technology was not ready for widespread rollout in this way. Indeed, in 2018, Joy Buolamwini, Timnit Gebru, and Deborah Raji—three Black female researchers at Microsoft—had published a study, alongside collaborators, comparing the accuracy of face recognition systems built by IBM, Face++, and Microsoft. They found the error rates for light-skinned men hovered at less than 1 percent, while that figure touched 35 percent for darker-skinned women. Karl knew that New Jersey resident Nijer Parks spent ten days in jail in 2019 and paid several thousand dollars to defend himself against accusations of shoplifting and assault of a police officer in Woodbridge, New Jersey.

The thirty-three-year-old Black man had been misidentified by a facial recognition system used by the Woodbridge police. The case was dismissed a year later for lack of evidence, and Parks later sued the police for violation of his civil rights.

A year after that, Robert Julian-Borchak Williams, a Detroit resident and father of two, was arrested for a shoplifting crime he did not commit, due to another faulty facial recognition match. The arrest took place in his front garden, in front of his family.

Facial recognition technology also led to the incorrect identification of American-born Amara Majeed as a terrorist involved in Sri Lanka’s Easter Day bombings in 2019. Majeed, a college student at the time, said the misidentification caused her and her family humiliation and pain after her relatives in Sri Lanka saw her face, unexpectedly, amongst a line-up of the accused terrorists on the evening news.

As his worlds started to collide, Karl was forced to reckon with the implications of AI-enabled surveillance—and to question his own role in it, acknowledging it could curtail the freedoms of individuals and communities going about their normal lives. “I think I used to believe that I create technology,” he told me, “and other smart people deal with policy issues. Now I have to ponder and think much deeper about what it is that I’m doing.”

And what he had thought of as technical glitches, such as algorithms working much better on Caucasian and male faces while struggling to correctly identify darker skin tones and female faces, he came to see as much more than that.

“It’s a complicated feeling. As an engineer, as a scientist, I want to build technology to do good,” he told me. “But as a human being and as a Black man, I know people are going to use technology inappropriately. I know my technology might be used against me in some manner or fashion.”

In my decade of covering the technology industry, Karl was one of the only computer scientists to ever express their moral doubts out loud to me. Through him, I glimpsed the fraught relationship that engineers can have with their own creations and the ethical ambiguities they grapple with when their personal and professional instincts collide.

He was also one of the few technologists who comprehended the implicit threats of facial recognition, particularly in policing, in a visceral way.

“The problem that we have is not the algorithms but the humans,” he insisted. When you hear about facial recognition in law enforcement going terribly wrong, it’s because of human errors, he said, referring to the over-policing of African American males and other minorities and the use of unprovoked violence by police officers against Black people like Philando Castile, George Floyd, and Breonna Taylor.

He knew the technology was rife with false positives and that humans suffered from confirmation bias. So if a police officer believed someone to be guilty of a crime and the AI system confirmed it, they were likely to target innocents. “And if that person is Black, who cares?” he said.

He admitted to worrying that the inevitable false matches would result in unnecessary gun violence. He was afraid that these problems would compound the social malaise of racial or other types of profiling. Together, humans and AI could end up creating a policing system far more malignant than the one citizens have today.

“It’s the same problem that came out of the Jim Crow era of the ’60s; it was supposed to be separate but equal, which it never was; it was just separate . . . fundamentally, people don’t treat everybody the same. People make laws, and people use algorithms. At the end of the day, the computer doesn’t care.”

Excerpted from Code Dependent: Living in the Shadow of AI by Madhumita Murgia. Published by Henry Holt and Company. Copyright © 2024 by Madhumita Murgia. All rights reserved.

[bandzboy]

i just wanna say that i've never seen stans from a kpop group be company stans as hard as armchairs are it's just disturbing how much they defend the company and the ceo like they are in the group themselves i promise you they don't gaf about you they just care about your money 😭

[toothpuulp]

truly nothing funnier to me than the i spit on your grave original tagline miscounting the number of rapists never ever have i seen a more blatant example of how the people who do movie marketing are a completely separate entity from the filmmakers also how humiliating for them to be even worse at counting than me. that’s like, an actual achievement

[thepresence360]

[third-eyeai]

The Expansive Applications and Benefits of Facial Recognition Technology

Facial recognition technology is revolutionizing the way organizations enhance security and optimize operational efficiency across various industries. This innovative biometric solution analyzes facial features to verify identities, offering a range of applications that not only bolster security but also streamline numerous processes. In this article, we will explore the various areas where facial recognition can be utilized, the substantial benefits it brings to organizations, and the challenges they might face without these advanced technologies.

Applications of Facial Recognition Technology

1. Gate Security Management

Facial recognition systems are increasingly deployed at entry and exit points in facilities such as corporate offices, manufacturing plants, and high-security zones. These systems allow organizations to manage who enters and exits by verifying identities in real-time. Advanced algorithms assess the facial features of individuals against a database of authorized personnel, granting access only to those who are recognized.

Benefits

Enhanced Security: By ensuring that only individuals with proper clearance can enter sensitive areas, organizations can drastically reduce the risk of unauthorized access and potential security threats.

Quick Identification Process: Real-time facial recognition expedites the identification of individuals, minimizing wait times at access points. This leads to smoother traffic flow and improves overall security efficiency.

Detailed Audit Trails: Most systems maintain logs of access events, allowing security personnel to track who accessed specific areas and when. This feature is invaluable for investigations following security incidents, helping organizations respond promptly to any breaches.

2. Time and Attendance Management

Facial recognition technology is transforming how organizations track employee attendance. By integrating facial recognition systems into timekeeping processes, companies can automate the clock-in and clock-out procedures. Employees simply look at a camera that recognizes their face, recording their attendance without the need for traditional timecards or manual entries.

Benefits

Reduction in Time Theft: By eliminating the potential for buddy punching — where one employee clocks in for another — organizations can significantly reduce labor costs and ensure accurate attendance records.

Data-Driven Insights for Productivity: The automated system collects detailed attendance data, enabling organizations to analyze employee attendance patterns, identify trends, and make informed decisions regarding workforce management.

User-Friendly Experience: The simplicity of facial recognition provides employees with a hassle-free check-in process. Instead of dealing with physical timecards or logging into systems, they can simply glance at the camera, enhancing convenience and job satisfaction.

3. Visitor Management

In many organizations, visitor management is crucial for maintaining security and ensuring a positive experience for guests. Facial recognition technology can streamline the visitor check-in process. Visitors can register using a facial recognition system, which verifies their identity against a pre-approved list before granting access.

Benefits

Increased Security Measures: By quickly identifying visitors and checking them against a database, organizations can prevent unauthorized individuals from entering their premises, enhancing overall safety.

Streamlined and Efficient Check-In Process: Guests no longer need to fill out lengthy paper forms or wait in long lines. The system can verify identities in seconds, significantly improving the visitor experience and reducing bottlenecks.

Comprehensive Visitor Data Collection: The technology can log visitor details, including entry and exit times, which can be used for security analysis and to enhance visitor experience in future engagements.

4. Payroll Management

Facial recognition can also play a pivotal role in streamlining payroll processes. By linking attendance records captured via facial recognition to payroll systems, organizations can automate the entire payroll cycle — from clocking in to processing payments.

Benefits

Efficiency in Payroll Processing: Automating the payroll system reduces the administrative burden on HR departments, allowing them to focus on more strategic tasks rather than manual data entry.

Accuracy in Compensation Calculations: By relying on verified attendance data, organizations can ensure that employees are compensated accurately for the hours they worked, minimizing disputes and errors in pay.

Reduction of Paperwork: Digitizing attendance and payroll processes helps organizations cut down on paper usage, contributing to more environmentally friendly practices while streamlining operations.

5. Field Force Management

For companies with field workers, managing attendance and performance can be challenging. Facial recognition technology provides a robust solution to monitor and verify the identities of field personnel in real-time. This allows organizations to keep track of who is on-site at any given moment.

Benefits

Real-Time Monitoring of Operations: Managers can receive immediate updates on field personnel attendance, allowing them to ensure that projects stay on schedule and that resources are allocated efficiently. Enhanced Accountability Among Workers: The knowledge that their attendance is being monitored encourages field workers to adhere to company policies and report to work as scheduled.

Streamlined Communication with Management: The system can provide instant feedback on attendance and performance metrics, enabling quicker decision-making and better resource management.

6. Canteen Management

Facial recognition technology can greatly enhance operations in employee canteens by automating meal purchases and tracking food balances. Employees can simply look at a camera to pay for their meals without the need for cash or cards.

Benefits

Precise Transaction Records: Automated systems accurately record each employee’s meal purchases, allowing for better inventory management and waste reduction.

Faster Payment Processing: With quick identification, employees can pay for their meals in seconds, significantly improving the dining experience during busy meal times.

Greater Employee Satisfaction: By streamlining the canteen process, organizations can enhance employee satisfaction and promote a positive workplace culture.

7. Employee Self-Service

Facial recognition technology can empower employees by enabling them to manage their attendance and schedules from mobile devices. With a simple facial scan, employees can check in and out, request leave, or update their availability.

Benefits

Increased Flexibility for Employees: This feature allows employees to manage their attendance without needing to physically report to HR, offering them greater flexibility and control over their schedules. Improved Accuracy in Reporting: With biometric verification, the chances of attendance reporting errors are significantly reduced, leading to more accurate workforce management.

Enhanced Employee Empowerment: By providing employees with the tools to manage their own attendance, organizations foster a sense of ownership and responsibility among their workforce.

8. Healthcare Applications

In healthcare environments, facial recognition technology can be used to verify patient identities and streamline the admissions process. Patients can simply check in using facial recognition, which confirms their identity and retrieves their medical records.

Benefits

Enhanced Patient Safety and Security: By ensuring that patients are accurately identified, healthcare providers can minimize the risk of medical errors and mix-ups.

Quicker Admissions Process: The streamlined check-in reduces waiting times for patients, allowing healthcare professionals to focus more on providing care rather than administrative tasks.

Improved Data Privacy: Biometric systems enhance patient data security, ensuring that only authorized personnel can access sensitive medical information.

9. Retail and Customer Experience

In the retail sector, facial recognition technology can be harnessed to analyze customer behavior and preferences, enabling personalized shopping experiences. Retailers can use this data to tailor marketing strategies and enhance customer engagement.

Benefits

Targeted Marketing Initiatives: By recognizing returning customers, retailers can offer personalized promotions and recommendations, improving customer loyalty and satisfaction.

Enhanced Security Against Theft: Facial recognition systems can identify known shoplifters, allowing retailers to take preventative measures to protect their inventory.

Operational Insights for Better Management: Analyzing customer traffic patterns helps retailers optimize store layouts and staff deployment for improved customer service.

Challenges Without Facial Recognition Technology

Despite the numerous benefits of facial recognition technology, organizations that do not adopt this innovative solution may face several challenges, including:

1. Increased Security Risks

Unauthorized Access: Without effective access control measures, organizations may experience higher rates of unauthorized entry, leading to potential security breaches.

Vulnerability to Threats: Traditional security measures, such as ID badges, can be easily compromised, putting organizations at risk.

2. Inefficiencies in Operations

Manual Processes: Organizations may rely on outdated manual systems for attendance tracking and visitor management, leading to increased labor costs and potential errors.

Long Wait Times: Inefficient visitor check-in processes can frustrate guests and lead to negative experiences.

3. Data Inaccuracies

Human Error: Manual record-keeping is prone to mistakes, which can affect payroll calculations and employee records.

Limited Insights: Organizations may miss valuable data that could be used to analyze trends and improve operational efficiency.

4. High Administrative Overhead

Labor-Intensive Tasks: Without automation, HR and security personnel may spend excessive time managing attendance, payroll, and visitor logs, diverting focus from strategic initiatives.

Increased Paperwork: Relying on paper-based systems can lead to clutter and difficulties in managing records.

5. Poor Customer Experience

Frustrated Visitors: Inefficient check-in processes can lead to long wait times, negatively impacting the visitor experience.

Missed Opportunities: Retailers may struggle to personalize customer experiences without data-driven insights from facial recognition technology.

Conclusion

Facial recognition technology offers vast applications across multiple sectors, significantly enhancing security, streamlining operations, and improving overall efficiency. While organizations face challenges without these technologies, embracing facial recognition can provide them with a competitive edge in today’s fast-paced environment.

As businesses increasingly adopt this innovative technology, they can expect improved efficiency, enhanced security measures, and a better experience for employees and customers alike. The future is bright for those willing to leverage facial recognition technology to navigate the complexities of modern operations.

[techcomengineering]

Unlocking Security: The Benefits of Modern Door Access Systems

In today’s security-focused landscape, traditional locks and keys are quickly becoming outdated. Modern door access systems are transforming how we protect our homes and businesses, providing innovative solutions that improve safety and convenience. Whether through keypads, key fobs, or biometric scanners, these systems offer flexible and effective methods to manage access. In this blog, we’ll examine the advantages of modern door access systems and explain why they are crucial for anyone aiming to enhance their security measures.

1. Enhanced Security

Modern door access systems provide superior security features compared to traditional locks. With options like biometric scanners, key fobs, and access codes, unauthorized entry becomes significantly more difficult. Many systems also offer real-time monitoring and alerts, allowing you to keep track of who enters and exits your property.

2. Convenience and Ease of Use

Gone are the days of fumbling for keys. Access systems streamline entry with user-friendly interfaces. Whether it’s a simple keypad or a smartphone app, users can gain access quickly and easily. This is particularly beneficial in commercial settings, where multiple employees need efficient entry.

3. Remote Access Control

One of the standout features of modern door access systems is the ability to control access remotely. Property owners can grant or revoke access from anywhere, using their smartphones or computers. This is especially useful for managing guest access or handling emergencies when you’re not on-site.

4. Audit Trails and Monitoring

Many access systems provide detailed logs of entry and exit times, creating an audit trail that can be invaluable for security. This feature not only enhances accountability but also allows for quick investigations in case of security incidents.

5. Scalability and Flexibility

Modern door access systems can easily scale to meet your needs. Whether you have a single entry point or multiple doors across different locations, these systems can be customized to fit your security requirements. This flexibility makes them ideal for growing businesses or expanding residential properties.

6. Integration with Other Security Systems

Access systems can often integrate seamlessly with other security measures, such as CCTV cameras and alarm systems. This holistic approach to security ensures that all aspects of your property are monitored and protected.

Investing in modern door access systems not only enhances security but also offers convenience and flexibility for users. As technology continues to evolve, these systems are becoming increasingly essential for safeguarding your property. Whether for your home or business, upgrading to a modern access system could be the key to unlocking a safer environment.

Are you considering a door access system? Share your thoughts in the comments below!

[okulr]

Face Recognition Using AI: Revolutionizing Identification

In recent years, face recognition technology has emerged as one of the most impactful applications of artificial intelligence (AI). Its ability to accurately identify individuals based on their unique facial features has transformed various sectors, making it a critical tool in security, attendance management, and more. As we delve into the intricacies of face recognition, we will explore how AI has enhanced this technology and its applications in everyday life, including the use of face recognition attendance systems, face recognition cameras, and face recognition CCTV systems.

Face recognition technology operates on the principle of biometric identification, utilizing distinctive facial features to verify or identify individuals. The journey of face recognition began in the 1960s, but it was not until the advent of machine learning and deep learning that the technology saw significant improvements. Early systems relied on manual processes and simple algorithms, which limited their accuracy. With the rise of AI, particularly deep learning, the landscape of face recognition changed dramatically. Modern systems now employ sophisticated algorithms capable of learning from vast datasets, allowing them to extract relevant features from faces without human intervention. This evolution has led to remarkable accuracy levels and increased versatility in various applications.

The process of face recognition typically begins with capturing an image of an individual’s face, which can be done using face recognition cameras or CCTV systems equipped with AI capabilities. High-resolution images are crucial for effective feature extraction, as they allow the system to analyze minute details. Once an image is captured, the system identifies key facial features, such as the distance between the eyes or the shape of the nose. Advanced AI algorithms, particularly convolutional neural networks (CNNs), efficiently extract these features. Following extraction, the system compares the captured facial features against a database of known faces, using complex algorithms to evaluate similarity. If a match is found, the individual is identified; if not, the system may flag the individual as unrecognized.

One of the most significant applications of face recognition technology lies in security and surveillance. Face recognition CCTV cameras are increasingly deployed in public spaces, such as airports, train stations, and city streets. These systems enhance security by quickly identifying individuals on watchlists and providing law enforcement with critical information in real-time. The benefits of using face recognition in security are numerous. For one, it allows for immediate alerts in the presence of known offenders or suspicious activities, ultimately contributing to crime prevention and improved public safety. Moreover, the ability to scan multiple faces simultaneously enhances the efficiency of security operations, making it a valuable tool in high-traffic environments.

In addition to security applications, face recognition technology has found a prominent place in attendance management systems. In educational institutions and corporate environments, these systems offer a seamless way to track attendance without the need for manual roll calls or sign-ins. By automating the attendance process, face recognition attendance systems save time for teachers and HR personnel, allowing them to focus on more critical tasks. Additionally, they minimize errors associated with manual attendance, ensuring accurate records of student and employee attendance. The convenience offered by these systems is unmatched; individuals can simply walk into a designated area, and their attendance is automatically recorded.

The retail and marketing sectors are also embracing face recognition technology to enhance customer experiences. By analyzing customer demographics through face recognition cameras, businesses can tailor their marketing strategies to target specific audiences more effectively. This personalized approach allows retailers to create unique shopping experiences that resonate with customers, ultimately driving sales and customer loyalty. Understanding customer behavior through face recognition data empowers businesses to optimize store layouts and product placements, further improving the shopping experience.

In the healthcare sector, face recognition technology plays a vital role in patient identification and data security. Accurate patient identification reduces the risk of medical errors associated with misidentification, ensuring that individuals receive the correct treatment and medication. Furthermore, secure access to patient records can be enforced through face recognition, protecting sensitive information and enhancing data security.

The advantages of using face recognition technology extend beyond its applications. One of the most significant benefits is its speed and efficiency. Traditional identification methods can be time-consuming, requiring manual checks or input. In contrast, AI-driven face recognition systems can verify identities within seconds, making them ideal for environments with high foot traffic. Moreover, the accuracy of these systems has improved significantly due to advancements in AI and machine learning. Modern face recognition systems can achieve accuracy rates exceeding 99%, drastically reducing the chances of false positives or negatives, which is crucial for applications in security and attendance management.

While the benefits of face recognition technology are substantial, several challenges and ethical considerations must be addressed. Privacy concerns are at the forefront of the discussion surrounding face recognition technology. Individuals may not be aware that their faces are being scanned and recorded, raising questions about consent and personal privacy. The potential for mass surveillance can create an environment of distrust, necessitating transparent practices in the deployment of such systems.

Another significant concern is the potential for bias and discrimination within AI algorithms. Studies have shown that face recognition systems may be less accurate for individuals with darker skin tones or certain demographic groups, reflecting and perpetuating biases present in the training data. Addressing these biases is critical to ensuring fair and equitable outcomes, especially as face recognition technology becomes more prevalent.

As we look to the future, the prospects for face recognition technology are promising. Ongoing advancements in AI and machine learning will continue to enhance the capabilities of face recognition systems, leading to higher accuracy rates and the ability to recognize individuals in various conditions, such as low light or from different angles. Additionally, the integration of face recognition technology into smart cities is expected to expand, improving urban security, streamlining transportation systems, and enhancing public services. For instance, smart traffic lights could utilize face recognition to prioritize emergency vehicles, showcasing the technology's versatility.

The evolution of AI in face recognition technology holds immense potential across various industries. From security applications utilizing face recognition CCTV cameras to innovative attendance management systems, the benefits of this technology are vast. However, it is crucial to address the ethical challenges and privacy concerns associated with its implementation. As we navigate this evolving landscape, a balanced approach that emphasizes responsible use and public trust will be essential in harnessing the full potential of face recognition technology.

In recent years, face recognition technology has emerged as one of the most impactful applications of artificial intelligence (AI). Its ability to accurately identify individuals based on their unique facial features has transformed various sectors, making it a critical tool in security, attendance management, and more. As we delve into the intricacies of face recognition, we will explore how AI has enhanced this technology and its applications in everyday life, including the use of face recognition attendance systems, face recognition cameras, and face recognition CCTV systems.

Face recognition technology operates on the principle of biometric identification, utilizing distinctive facial features to verify or identify individuals. The journey of face recognition began in the 1960s, but it was not until the advent of machine learning and deep learning that the technology saw significant improvements. Early systems relied on manual processes and simple algorithms, which limited their accuracy. With the rise of AI, particularly deep learning, the landscape of face recognition changed dramatically. Modern systems now employ sophisticated algorithms capable of learning from vast datasets, allowing them to extract relevant features from faces without human intervention. This evolution has led to remarkable accuracy levels and increased versatility in various applications.

The process of face recognition typically begins with capturing an image of an individual’s face, which can be done using face recognition cameras or CCTV systems equipped with AI capabilities. High-resolution images are crucial for effective feature extraction, as they allow the system to analyze minute details. Once an image is captured, the system identifies key facial features, such as the distance between the eyes or the shape of the nose. Advanced AI algorithms, particularly convolutional neural networks (CNNs), efficiently extract these features. Following extraction, the system compares the captured facial features against a database of known faces, using complex algorithms to evaluate similarity. If a match is found, the individual is identified; if not, the system may flag the individual as unrecognized.

One of the most significant applications of face recognition technology lies in security and surveillance. Face recognition CCTV cameras are increasingly deployed in public spaces, such as airports, train stations, and city streets. These systems enhance security by quickly identifying individuals on watchlists and providing law enforcement with critical information in real-time. The benefits of using face recognition in security are numerous. For one, it allows for immediate alerts in the presence of known offenders or suspicious activities, ultimately contributing to crime prevention and improved public safety. Moreover, the ability to scan multiple faces simultaneously enhances the efficiency of security operations, making it a valuable tool in high-traffic environments.

In addition to security applications, face recognition technology has found a prominent place in attendance management systems. In educational institutions and corporate environments, these systems offer a seamless way to track attendance without the need for manual roll calls or sign-ins. By automating the attendance process, face recognition attendance systems save time for teachers and HR personnel, allowing them to focus on more critical tasks. Additionally, they minimize errors associated with manual attendance, ensuring accurate records of student and employee attendance. The convenience offered by these systems is unmatched; individuals can simply walk into a designated area, and their attendance is automatically recorded.

The retail and marketing sectors are also embracing face recognition technology to enhance customer experiences. By analyzing customer demographics through face recognition cameras, businesses can tailor their marketing strategies to target specific audiences more effectively. This personalized approach allows retailers to create unique shopping experiences that resonate with customers, ultimately driving sales and customer loyalty. Understanding customer behavior through face recognition data empowers businesses to optimize store layouts and product placements, further improving the shopping experience.

In the healthcare sector, face recognition technology plays a vital role in patient identification and data security. Accurate patient identification reduces the risk of medical errors associated with misidentification, ensuring that individuals receive the correct treatment and medication. Furthermore, secure access to patient records can be enforced through face recognition, protecting sensitive information and enhancing data security.

The advantages of using face recognition technology extend beyond its applications. One of the most significant benefits is its speed and efficiency. Traditional identification methods can be time-consuming, requiring manual checks or input. In contrast, AI-driven face recognition systems can verify identities within seconds, making them ideal for environments with high foot traffic. Moreover, the accuracy of these systems has improved significantly due to advancements in AI and machine learning. Modern face recognition systems can achieve accuracy rates exceeding 99%, drastically reducing the chances of false positives or negatives, which is crucial for applications in security and attendance management.

While the benefits of face recognition technology are substantial, several challenges and ethical considerations must be addressed. Privacy concerns are at the forefront of the discussion surrounding face recognition technology. Individuals may not be aware that their faces are being scanned and recorded, raising questions about consent and personal privacy. The potential for mass surveillance can create an environment of distrust, necessitating transparent practices in the deployment of such systems.

Another significant concern is the potential for bias and discrimination within AI algorithms. Studies have shown that face recognition systems may be less accurate for individuals with darker skin tones or certain demographic groups, reflecting and perpetuating biases present in the training data. Addressing these biases is critical to ensuring fair and equitable outcomes, especially as face recognition technology becomes more prevalent.

As we look to the future, the prospects for face recognition technology are promising. Ongoing advancements in AI and machine learning will continue to enhance the capabilities of face recognition systems, leading to higher accuracy rates and the ability to recognize individuals in various conditions, such as low light or from different angles. Additionally, the integration of face recognition technology into smart cities is expected to expand, improving urban security, streamlining transportation systems, and enhancing public services. For instance, smart traffic lights could utilize face recognition to prioritize emergency vehicles, showcasing the technology's versatility.

The evolution of AI in face recognition technology holds immense potential across various industries. From security applications utilizing face recognition CCTV cameras to innovative attendance management systems, the benefits of this technology are vast. However, it is crucial to address the ethical challenges and privacy concerns associated with its implementation. As we navigate this evolving landscape, a balanced approach that emphasizes responsible use and public trust will be essential in harnessing the full potential of face recognition technology.

[jcmarchi]

A mindset for fairer AI in criminal justice

New Post has been published on https://thedigitalinsider.com/a-mindset-for-fairer-ai-in-criminal-justice/

A mindset for fairer AI in criminal justice

Artificial intelligence (AI) is being discussed nearly everywhere these days, including in legal circles. AI promises efficiency and objectivity, which are sorely needed in the justice system, but there are also horror stories, including racial bias against criminal defendants and even innocent individuals being wrongfully arrested.

The root cause often lies in the inherent biases within some algorithms that power AI systems, but the problem runs deeper than that. It’s also about the data the systems are trained on, the goals we set for AI systems, how those are applied, and how we interpret the results. It’s not just technology – it’s us.

Enter Public Interest Technology (PIT), which we can think of as an essential mindset that focuses us on selecting, implementing, and evaluating AI systems in ways that are fair, just, and human-centered. It’s an approach that sets our sights squarely on the decisions that are most important when it comes to protecting people from the actual harms of bias and discrimination.

Public Interest Technology can act as a guiding framework that supports the development, implementation, and governance of AI in the criminal justice system to ensure fairness, transparency, and accountability.

What exactly is Public Interest Technology?

Public Interest Technology is a human-centered approach to technology that prioritizes social justice, fairness, and equity in the design, development, and implementation of technological solutions.

Darren Walker, president of the Ford Foundation, explains that PIT focuses less on the technology itself and more on ethics, human rights, and social justice [1]. It emphasizes a socio-technological approach that prioritizes people’s needs over unchecked technological development. In essence, PIT seeks to ensure that technology serves us and not the other way around.

This means designing, using, and regulating technology to benefit everyone, especially those from vulnerable or historically marginalized groups. It’s about making sure everyone has a say in decisions about the tech that affects their lives.

AI in justice contexts

AI is already used in the criminal justice system to identify suspects, predict re-offense risk, and suggest criminal sentences. These are all powerful tools that promise to improve justice outcomes and positively affect society as a whole. 

However, these same tools can and have perpetuated discrimination when not carefully and thoughtfully applied. 

According to the ACLU, “…there have been at least seven wrongful arrests we know of in the United States due to police reliance on incorrect face recognition results — and those are just the known cases. In nearly every one of those instances, the person wrongfully arrested was Black” [2].

Further, recidivism prediction tools, such as COMPAS, have been criticized as unfairly categorizing Black men as high-risk for reoffense when compared to their White counterparts [3].  Some criminal courts are using this information to inform the sentencing decisions judges make [4]. Even worse, these AI tools are often opaque, meaning the decision-making processes they use are either unclear or entirely unknown.

Tackling algorithmic bias head-on

Algorithmic bias in facial recognition and recidivism prediction tools occurs in part due to biased data, poorly devised algorithms, and problematic feature sets.  But it’s also due to a lack of human guidance and governance structures that restrain, shape, and guide the safe implementation of the technology. PIT not only emphasizes improving the technology itself but also stresses continued human management of those systems to recognize, address, and eliminate biased outcomes altogether.

For instance, researchers in New Zealand are developing transparent models for assessing assault cases in criminal courts [5]. Unlike the COMPAS program described above, these researchers are developing transparent AI models that open the model’s decisions to scrutiny. By making the inner workings of the AI clear, it’s easier to identify and correct potential biases and thereby prevent harm. 

This aligns with the core PIT principles of transparency and accountability that contribute to fair outcomes and societal trust in these systems.

Human in the Loop

In addition to improving transparency, PIT also highlights the importance of human oversight. The concept of having a human in the loop is mandatory to ensure fairness, accountability, and transparency [6]. AI can be powerful in many respects, but it cannot replace human judgment, especially in high-stakes settings like the justice system. 

Humans should not only be involved in developing and using AI, but they should always have the power to override AI-based decisions in any given case. This doesn’t guarantee fairer outcomes (human judges can be biased, too), but it does create accountability for the final result. It’s impossible to hold an algorithm accountable. It’s entirely possible to criticize and potentially remove an unfair judge.

A fairer tech future

PIT isn’t a magic solution. Mindsets alone will not solve the problems that AI poses to society. However, it does focus our attention on implementing AI systems in ways that promote justice and equity, especially in the most sensitive of areas, like the criminal justice system. 

By upholding values like fairness, transparency, and human oversight, PIT can help us minimize AI risks and ensure that this powerful technology serves society as a whole.

As AI becomes further intertwined with our lives, PIT will become even more crucial. By working together – technologists, policymakers, advocates, and the public – we can build a future where AI is a force for good, not harm. 

After all, technology should always be a tool for justice, not a weapon of discrimination.

References

[1] Walker, D. (n.d.). Deprogramming Implicit Bias: The Case for Public Interest Technology. https://doi.org/10.1162/daed_a_02059

[2] Wessler, N. F. (2024, April 30). Police Say a Simple Warning Will Prevent Face Recognition Wrongful Arrests. That’s Just Not True. | ACLU. American Civil Liberties Union. https://www.aclu.org/news/privacy-technology/police-say-a-simple-warning-will-prevent-face-recognition-wrongful-arrests-thats-just-not-true#:~:text=To%20date%2C%20there%20have%20ben

[3] Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

[4] Hao, K. (2019, January 21). AI Is Sending People to Jail—and Getting It Wrong. MIT Technology Review. https://www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/

[5] Rodger, H., Lensen, A., & Betkier, M. (2022). Explainable artificial intelligence for assault sentence prediction in New Zealand. Journal of the Royal Society of New Zealand, 53(1), 133–147. https://doi.org/10.1080/03036758.2022.2114506

[6] Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2022). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56. https://doi.org/10.1007/s10462-022-10246-w

Interested to know more about bias and the human mind?

Make sure to give the article below a read:

A snapshot of bias, the human mind, and AI

Understanding human bias, AI systems, and leadership challenges in technology management and their impacts on decision-making.

[rfid-sistemleri]

Suprema Erişim ve Personel Kontrolü

Suprema, biyometrik güvenlik çözümleri ve erişim kontrol sistemleri alanında tanınmış ve güvenilir bir markadır. Özellikle parmak izi tanıma ve yüz tanıma teknolojisi ile öne çıkan Suprema, kartlı geçiş çözümlerinde de geniş bir ürün yelpazesi sunmaktadır.

Kart Okuyucular

XPass: Dayanıklı ve güvenilir proximity kart okuyucular. RFID teknolojisi ile çalışır ve IP65 dereceli koruma sağlar.

XPass S2: Kompakt ve şık tasarıma sahip proximity kart okuyucu. IP65 ve IK08 dereceli koruma ile dış mekân kullanımı için uygundur.

Biyometrik Kartlı Okuyucular

BioEntry W2: Hem parmak izi hem de kart okuyucu özelliğine sahip çift katmanlı güvenlik sunar. IP67 dereceli koruma ile zorlu çevre koşullarına dayanıklıdır.

BioEntry R2: Küçük boyutlu ve şık tasarımı ile dikkat çeker. Parmak izi ve kart okuyucu özelliklerini bir arada sunar.

Kontrol Panelleri

CoreStation: Merkezi kontrol ünitesi, büyük ölçekli erişim kontrol sistemleri için tasarlanmıştır. Yüksek performans ve esneklik sağlar.

Yazılım

Meyer Angel: Suprema cihazları ile çalışabilen web tabanlı erişim kontrol ve puantaj yazılımıdır. Kullanıcı dostu arayüzü ve geniş entegrasyon seçenekleri ile esneklik sunar. Türkiye'nin En İyi PDKS yazılımıdır.

Avantajları

Güvenilirlik: Suprema, Dünya kalitesinde yüksek güvenlik standartları ve dayanıklılığı ile bilinir.

Çok Yönlülük: Parmak izi, kart, yüz tanıma, barkod ve QR kod gibi çeşitli kimlik doğrulama yöntemlerini destekler.

Entegrasyon: Suprema cihazları, diğer güvenlik ve yönetim sistemleri ile kolayca entegre edilebilir.

Kullanıcı Dostu: Kolay kurulum ve kullanım sağlayan yazılım ve donanım çözümleri sunar.

Kullanım Alanları

Ofis ve İşyerleri: Güvenli giriş çıkış kontrolü, ziyaretçi yönetimi.

Üniversiteler ve Okullar: Öğrenci ve personel erişim kontrolü.

Sağlık Kurumları: Hastane personeli ve ziyaretçi yönetimi.

Otel ve Konaklama Sektörü: Misafir odası erişimi, otopark yönetimi.

Kamu Binaları: Güvenli alanlara erişim kontrolü, kimlik doğrulama.

Endüstriyel Tesisler: Üretim alanları ve depolar için güvenlik.

Diğerleri: Erişim kontrolü gerektiren her alanda kullanılabilir.

Suprema'nın ürünleri, güvenilir ve esnek çözümler sunarak, yüksek güvenlik ve kullanıcı memnuniyeti sağlamaktadır. Bu özellikleriyle Dünya çapında pek çok kurum ve işletme tarafından tercih edilmektedir.

[expediteiot]

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[astiinfotech1]

Facial recognition systems need a database or a pre-recorded data set to compare captured images and identify faces. A complete high-end configuration unit is installed in the institute and the data capturing process is initiated. The camera mounted with the machine captures and processes the images of students with various angles and qualities along with the basic identification details for further processing.The Image is processed in this way to take care of image quality & other factors.

[biocube-technologies-inc]

TruNtrance: Redefining Employee Activity Monitoring & Time Attendance

Even in the age of technological innovations, employee monitoring and tracking have become a cumbersome issue. Enterprises not only have to install sophisticated and costly specialized hardware systems but are also required to learn about the challenging functions they incorporate.

The complicated view of the location tracking software increases the burden on HR and administrative department staff. Even after such a chaotic learning process, the employee tracking systems remain vulnerable to spoofing and tampering with activities.

Activities like buddy punching lead to the wrongful calculation of wages, especially for a contractual and remote workforce. Besides, they also give a wrongful impression of the employee's productivity hours and refrain the more productive workers from coming into the limelight.

We at Biocube understand the severity of the worker management system and cater to the growing demands of enterprises with our newest feature of activity monitoring. Our new feature not only gives a clear picture of workers' overall productive hours but also enhances the security and convenience of the enterprise.

TruNtrance Real-Time Activity Monitoring & Tracking is a Must for Your Enterprise!

TruNtrance is an employee attendance, visitor access management, and activity monitoring solution that streamlines workforce attendance at the premises or remotely. It enhances the visitor experience with its facial recognition technology. 

Recently, Biocube incorporated a new feature in Employee Activity Monitoring into the app to enhance the dashboard experience of HR and admin staff while revamping worker security and convenience.  

The new feature allows an employee or a contractual worker to mark an activity like event visitation, arrival/departure from a remote location, meeting, etc, with spoofproof face recognition. It will accurately capture the geolocation of the employee whenever it detects a registered face. 

To put simply, the employee activity monitoring systems use face recognition to mark the geolocation and activity start & end durations. It comes with built-in liveness detection, military-grade AES 256 encryption, distributed data architecture, and geofencing. 

Hence, it leaves no room for tampering or spoofing, and the employee is required to be present at the location to mark the activity. The dashboard would reveal insights like the types of activities, time spent on each activity, and more. Moreover, the dashboard is fully customizable to curate insights desired by organizational stakeholders.

Thinking About Geo-Attendance?

Geo-attendance has become a trending topic in the attendance management market. However, such a solution needs to be more accurate. Theoretically, it marks employee activity or attendance whenever the worker arrives at the location or checks out from it. Anyone can tamper with it, as any other coworker can carry the device. Therefore, we came up with a system to resolve the geo attendance.

Our next-gen geolocation system seamlessly tracks and monitors the activities using the face of the registered user. As mentioned above, it has no room for tampering or spoofing because it inhibits many security features. It also incorporates an additional "Notes" feature that eases the burden of the employees who continuously engage in remote or fieldwork.

Conclusion 

Employee activity tracking and monitoring is an essential part of organizations. Knowing about time spent on each activity helps to curate accurate wages of remote or temporary employees. Moreover, it helps in understanding the time required to complete an activity.

Such information can be helpful in deciphering decisions that can boost the productivity of the employee and benefit the organization. Biocube has recently incorporated a new feature of Activity driven by face recognition and geolocation tracking.

The new feature helps to derive insights that are usable by administrative and HR departments. Get in touch with us today to see the feature in action and learn more about the ways through which we can enhance your organization's efficiency. 

[thepresence360]

[third-eyeai]

Stay competitive in the manufacturing industry by integrating our advanced Biometric Facial Recognition Solution, specifically created to optimize workforce management and strengthen security The dynamic environment of manufacturing facilities, our solution offers precise employee attendance tracking, secure access control, and regulatory compliance assurance. With seamless integration into your existing operations, ThirdEye AI’s Facial Recognition System improves security measures, boosts operational efficiency, and ensures smooth compliance management. Elevate your manufacturing processes with cutting-edge biometric technology today