Face Detection & Facial Recognition: Mastering the Basics

Face Detection & Facial Recognition
Photo source: Shutterstock/Fractal Pictures
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In an age where technology is advancing at a breakneck pace, face detection and facial recognition technology are emerging as crucial tools for various applications.

These rapidly growing technologies have entered multiple fields, including law enforcement, advertising, and social media. Intrinsically, it has become increasingly necessary to understand the capabilities and potential benefits and drawbacks of these technologies.

With this in mind, this blog will explore the basics of face detection and facial recognition, their importance, and the latest trends and issues in the field.

What is Face Detection?

Face detection is the technology that can locate and recognize human faces within a digital image or video. It is the first step in facial recognition systems. However, it only involves identifying the presence of a face, not who that person is.

The process of face detection involves analyzing the pixels in the image or video frame to identify shapes that resemble a human face and then using various techniques to determine whether the shape is indeed a face.

Think of it like this: when you take a picture with your smartphone or digital camera, the device can use face detection to identify where the faces are in the image and focus on them. This is why you might see a little box or square around a face on your camera’s screen. 

What is Facial Recognition? 

Facial recognition is a technology that goes beyond just detecting a face. It is a type of biometric authentication that uses algorithms to analyze and compare images of faces. It is a subset of the larger field of computer vision, which deals with automated visual data analysis.

Facial recognition works by capturing an image of a person’s face and comparing it to a database of faces to find a match. This process involves complex algorithms and machine learning to analyze and interpret facial features.

It can identify specific individuals by analyzing unique features of their faces, such as the distance between their eyes, the shape of their jawline, and the contour of their cheekbones. 

The market for facial recognition, a form of pattern recognition technology, is projected to increase to $16.7 billion in 2030, and the technology has seen a significant increase in accuracy. The current error rate is a mere 0.8 percent, compared to 4.1 percent less than a decade ago.

Video source: YouTube/ Interesting Engineering

What is the Difference Between Face Detection and Facial Recognition?

In the world of computer vision, face detection and facial recognition technologies are often used interchangeably. However, they are actually two distinct processes.

The key difference between the two technologies is that face detection is a more basic and general process. At the same time, facial recognition is a more advanced and specific process that requires machine learning algorithms.

It is essential to comprehend the differences between these two technologies and their potential benefits and drawbacks. 

How Face Detection and Facial Recognition Work

Facial recognition and face detection are among today’s most prominent cognitive computing applications. But how do they work? 

The Process of Capturing, Detecting, and Recognizing Faces

Facial recognition technology (FRT) involves capturing, detecting, and recognizing faces with the help of facial analysis.

The first step in  facial analysis is capturing an image of a face using a camera, which can be either a still image or a video feed. Next, the camera captures the image and sends it to a processing unit where the image is analyzed, and certain features are extracted.

The software used in these systems identifies 80 nodal points on a human face, which are endpoints employed to estimate variables of a person’s face. These variables include:

  • Length or width of the nose,
  • The depth of the eye sockets,
  • The shape of the cheekbones.

Once the facial recognition software captures the data for nodal points on an individual’s face digital image, it stores the resulting data as a faceprint. This faceprint is then used to compare data gathered from faces in an image or video.

Despite only using 80 nodal points, the system can accurately identify individuals under favorable conditions. However, the system becomes less reliable when a subject’s face is in profile or partially obscured.

In fact, since 1993, the frequency of false positives in facial recognition systems has been biennially halved, according to the National Institute of Standards and Technology (NIST). 

The Role of AI and Machine Learning in FRT

With the advent of machine learning and artificial intelligence, face detection and facial recognition technology development have been made possible.

These technological advancements rely on image processing techniques to extract features from input images. The images are then fed into machine learning algorithms.

Complex algorithms then use deep learning neural networks to analyze large amounts of data – in this case, photographs – to learn the difference between a face and other objects.

One of the major benefits of facial recognition technology is its processing speed. For example, it can scan large crowds in real-time, allowing for rapid identification of individuals within seconds.

Different methods may also be combined to eliminate errors. Experts stress the importance of using diverse images to train computers, as other races or non-binary genders can trip up face detection software

Applications of Face Detection and Facial Recognition

FRT is finding its way into many aspects of everyday life. From unlocking an iPhone to auto-tagging Facebook photos and from employers monitoring productivity to police forces observing protests.

Video source: YouTube/Analytics Insight

The applications of this technology can be grouped into three top categories: 

1. Backing Law Enforcement Efforts

One of the most notable applications of facial recognition technology is law enforcement. Agencies can leverage it to find missing people, prevent and detect crime, find wanted criminals, safeguard vulnerable people, and protect people from harm.

The common uses of FRT for policing are:

  • As a real-time aid to assist officers in locating people who are on a “watchlist” (sought by the police);
  • As an operator-initiated tool for officers who decide to take an image of a person and then use facial recognition software to establish who the person is, even if that person provides false or misleading details. This can also help provide an identification of someone unconscious or seriously injured and unable to communicate who they are;
  • As a retrospective system used after an event to help officers establish who a person is or whether their image matches against other media held on databases.

Regarding the potential impact of FRT use in law enforcement, 46% of American adults say its widespread use by police would benefit society.

In comparison, 27% believe it would be a bad idea. An additional 27% are still determining whether it would be right or wrong for the police to use facial recognition technology widely. 

2. Increasing Medical Treatment and Diagnoses Preciseness

Implementing FRT in healthcare has improved countless operations, from improved security and patient identification to better patient monitoring and diagnosis. Furthermore, it has helped improve patient experience and reduce the workload for healthcare workers.

Thanks to significant advances in deep learning and face analysis, it is already possible to:

  • Track a patient’s intake of medication more accurately,
  • Aid pain management procedures,
  • Diagnose rare genetic disorders in the early stage.

For better patient insights, face recognition can be blended with complementary technologies like real-time facial emotion recognition (the technology that analyses facial expressions). For example, it can monitor patients’ health status, detect pain, or even identify some illnesses’ symptoms.

Overall, the technology provides a non-invasive and contact-free solution, which is a significant advantage for various patients, particularly children who are afraid of invasive procedures or healthcare environments. 

3. Making Banking Easier and Shopping More Efficient

Banks and retailers progressively use technology to improve customer service and streamline operations:

Banking and FRT

Smart banking employs FRT to rid the system of existing pain points, creating a streamlined, tailored experience. In addition, the technology provides ease of service, adaptability, security, and a more satisfactory customer journey.

The best facial recognition solutions for banking provide the above advantages and cover various use cases, ranging from ATM identity verification and mobile banking to identity recognition for customer service and personalized experiences.

Records show that out of approximately 11,000 American financial institutions, 15% to 20% use selfie photo imaging along with document verification to authenticate users. It is expected this number will only increase. 

Retail and FRT

The adoption of facial recognition systems has gained attention as an all-in-one solution. That includes enhanced store security, personalized shopping experiences for loyalty program shoppers, contactless payment possibilities, and helpful customer analytics.

For a personalized experience, facial recognition is pivotal in identifying the shoppers based on age, gender, location, etc. After the shopper’s facial identification, a deep learning algorithm gives purchasing recommendations based on digital signage on past purchases.

A California burger chain of restaurants, CaliBurger, has installed kiosks with face recognition technology. While visiting the store, customers scan their faces, and with facial recognition, the kiosk screen will display their past and favorite purchases as a part of their loyalty program.

This way, the retail chain speeds up ordering when dealing with the workforce deficiencies.

Video source: Vimeo/Tech Feed 

Privacy and Ethical Concerns

While facial recognition technology has benefits, such as improved security and more efficient service delivery, it has also sparked concerns about privacy and ethics. The potential for misuse of the technology has raised alarms, prompting calls for greater regulation and oversight. 

1. Abuse of Power

One of the biggest ethical concerns is the potential for abuse of power. With the ability to track and monitor individuals without their knowledge or consent, there is a risk that governments or corporations could use facial recognition technology to infringe on people’s rights and freedoms. 

2. Bias

Another concern is the potential for discrimination, especially against marginalized groups. For example, there have been instances where facial recognition technology has exhibited racial or gender bias, leading to misidentifications and false arrests.

Again, this highlights the need for better training data and algorithms free from bias. 

3. Violation of Individual Privacy

Moreover, privacy concerns arise because facial recognition technology can collect and store sensitive data without individuals’ knowledge or consent.

This data can be utilized for targeted advertising, social engineering, or other nefarious purposes, leading to privacy and data protection law violations. 

4. Need for Greater Transparency

As such, policymakers, industry leaders, and privacy advocates call for greater transparency, accountability, and oversight regarding facial recognition technology.

They are advocating for the implementation of ethical standards, the protection of human rights, and the safeguarding of individual privacy.

Video source: Youtube/Civil Liberties Union for Europe 

Latest Trends in Face Detection and Facial Recognition

The latest trend in face detection and facial recognition is an increased focus on privacy and ethical concerns, as well as the development of more advanced algorithms and techniques.

With the growing use of facial recognition in various applications, such as law enforcement, advertising, and social media, there are increasing concerns about the potential for misuse, abuse, and violation of privacy.

Many countries and organizations have responded to these concerns by implementing regulations and guidelines for using FRT.

For example, the European Union proposed new rules on artificial intelligence in 2022. These rules included a ban on the use of real-time facial recognition in public spaces, with some exceptions for law enforcement and surveillance and security purposes.

In the United States, some cities and states have also implemented bans or moratoriums on using facial recognition technology by law enforcement agencies.

Another trend is the development of more advanced algorithms and techniques for face detection and facial recognition.

Researchers are also exploring new approaches, such as using brain-inspired neural networks and 3D face recognition, to improve the robustness and accuracy of facial recognition.

Wrapping Up On Face Detection and Recognition 

Face detection and facial recognition are advancing rapidly, with many practical applications in various industries. They offer the potential for greater efficiency and security but also raise significant concerns about privacy and ethics.

On one hand, these technologies can be incredibly powerful tools for:

  • Backing law enforcement efforts,
  • Increasing medical treatment and diagnoses preciseness,
  • Making banking easier and shopping more efficient.

However, there are certain drawbacks to using these technologies which include:

  • Abusing power,
  • Violating individual privacy, 
  • Perpetuating bias.

It is important to approach these technologies with a critical eye. We must ensure that their use is transparent and accountable.

Also, they need to be designed and deployed in ways that protect the privacy and rights of individuals.

As with any emerging technology, we must balance the potential benefits with the potential risks and ensure that the ethical and social implications are carefully considered.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.