Face recognition apps: How technology sees us

Face recognition apps: How technology sees us

A world where cameras on every corner know your face. Where you can unlock a smartphone with a glance, and criminals in a crowd can be identified in seconds. This once sounded dystopian, but today, thanks to face recognition technology, it has become a reality.

In recent years, facial recognition algorithms have evolved at breakneck speed. They have changed the way we think about security, privacy, and even what it means to be human.

Here, we will explore facial recognition technology, its opportunities and challenges, and when this technology will become even more powerful.

Where face recognition algorithms are used: Current trends

Face recognition technology is actively developing and finding more applications in various spheres. In 2024, human recognition systems are being used for:

  • Security. Control of access to buildings and premises and identification of persons. Prevention of theft and other crimes. Recognition of criminals and search for missing persons.
  • Law enforcement. Crime investigation, identification of suspects, surveillance of public places, traffic enforcement.
  • Commerce. Authorizing payments, personalizing promotional offers, improving customer service, and analyzing customer behavior.
  • Healthcare. Patient identification, medical records access control, disease diagnosis, and patient monitoring.
  • Education. Control access to educational institutions. Authorize the use of educational materials, track attendance, and personalize learning.
  • Public services. Issuance of documents, voting, receiving social benefits, and registration in state institutions.
  • Entertainment. Access to content, personalization of recommendations, games, and social networking.
  • Transportation. Biometric passenger identification with live facial recognition, vehicle access control, and fare automation.
  • Inclusiveness. Helping people with disabilities, making the environment accessible.
  • Innovation. Developing new applications and services, improving operational efficiency, and creating new capabilities.

More organizations are turning to artificial intelligence technology consultants every year, and the market for facial recognition algorithms is growing steadily. According to a report on an analysis of the facial recognition systems market by Grand View Research, it was valued at $5.15 billion in 2022. The market is also projected to grow at an average annual rate of 14.9% through 2030.

Facial recognition market scope

How does face recognition work?

Every person has characteristic physiological features. His or her personality can be relatively reliably identified by them. For example:

  • Eye shape and color
  • The shape and color of the hair
  • The size and shape of the nose
  • Eyebrow line
  • Lip line

For computer technology to "see" a person, it must take a photo and break it down into small fragments. The above characteristics are then extracted from the image and compared to a database. The result is a percentage ratio. It shows which combination of characteristics corresponds to which person.

Facial recognition work example

Here is the breakdown of how facial recognition works in 7 basic steps:

  1. Data collection. No face-matching app will work if there is no database to compare photos against. The system must first collect data containing images of faces. This data can be obtained from various sources, such as photographs or video recordings.
  2. Pre-processing. It is performed to make the analyzed images more homogeneous and prepare them for further analysis. It may include resizing, image quality improvement, illumination normalization, and so on.
  3. Feature extraction. In this step, the system analyzes the image and extracts features that may be unique to each face. These features can include eye location, nose, mouth, or face shape. Typically, machine learning techniques, such as convolutional neural networks, are used for this purpose.
  4. Face template creation. Based on the extracted features, the system creates a unique face template. It can be used for subsequent comparison with other faces from the database.
  5. Comparison and recognition. The algorithm compares the generated face template with others in the database. This can be done using various comparison methods, such as Euclidean distance or cosine similarity. If there is a match, the face is recognized.
  6. Decision-making. Based on the comparison results, the system decides whether to recognize the face. This decision may depend on the task for which the facial recognition system is used.
  7. Updating the database. When the system is used for verification or identification (for example, a biometric face recognition system), the recognition results can be used to update the database or take appropriate action.

All advanced face recognition technologies use a unique image processing algorithm, but they may differ in the method of face capture.

Traditional method

Facial features are taken through key points or features of the subject's face. The algorithm may analyze, for example, how the eyes are positioned relative to the nose. Or calculate the shape of the facial bones. These characteristics are then used to find a database of other images with the same features.

Three-dimensional recognition

A method that is robust to changes in lighting and angle because it uses 3D sensors to capture the image. They read the contours of a person's eye sockets, nose, and chin.

Analyzing skin texture

All the unique lines, patterns, and blemishes present on a person's skin are translated into a mathematical space.

Texture analysis works the same way as facial recognition. A skinprint (a snapshot of an area of skin) is broken down into small parts that are digitized. Then, the system begins to discern the topology of the pattern and "sees" the pores and the actual texture of the skin.

It can find the difference between identical pairs. This cannot be achieved with facial recognition software alone. Tests show that additional skin texture analysis can increase facial recognition results by 20–25%.

Thermal cameras

This method is relevant when a person's face is covered with thick makeup or glasses. In addition, thermal cameras can capture images even in low-light conditions. This equipment is usually based on segment-electric sensors. They have low sensitivity and resolution. Because of this, they can capture the thermal region of long-wave infrared (LWIR) radiation.

Each method has its own advantages and disadvantages. Therefore, companies developing human recognition systems usually combine all of the above methods.

The combined method has an advantage over other systems. It is relatively insensitive to facial expressions. For example, it "sees" whether a person is frowning or smiling and ignores blinking. It is also able to predict mustache or beard growth and recognize a person wearing glasses. This helps solve many of the face recognition problems we'll discuss below.

The history of facial recognition and its use in business

The pioneers of computer vision were scientists Woody Bledsoe, Helen Chan Wolf, and Charles Bisson. In 1964, they created the first prototype of a facial recognition system. It had many shortcomings but demonstrated the potential of this technology.

An image of Woody Bledsoe
An image of Woody Bledsoe from a 1965 study. The computer failed to recognize that two photos of him, from 1945 and 1965, showed the same person.
Source: Wired

The task was also complicated by the huge variation in the appearance of the faces, which depended on the angle of illumination or head tilt.

Since the early nineties, neural networks have been actively developed. They made it possible to automate the process of determining the coordinates of facial elements. The emergence of more powerful computers also made it possible to process images faster and more efficiently. Even live facial recognition is now possible. Previously, only face recognition from photos was available.

The most popular facial recognition algorithms include:

  • DeepFace. Developed by Facebook. Built on deep learning neural networks, it is capable of achieving high accuracy in face recognition. It is effective under different lighting conditions, face rotation, and other factors.
  • FaceNet. Developed by Google. Also uses deep learning to create vector representations of faces that can be compared to each other. Capable of working with large datasets.
  • OpenFace. Free face recognition software. An open-source library for face recognition that is based on neural networks. Popular due to its availability and ability to run in real-time on a variety of devices.
  • DeepID. Also uses deep neural networks to recognize faces. It is known for its high accuracy and ability to process large amounts of data.

The most widely used adoption of facial recognition technology is user authentication in smartphones. The feature is implemented on Apple, Google, and Microsoft devices. Google has also integrated recognition technology into Google Photos and uses it to sort images. It also automatically tags people in photos in the app.British Airways scans the faces of passengers who check in when boarding flights. In this way, their customers may not have to show their passports or boarding passes.McDonald's used facial recognition in its Japanese restaurants to evaluate customer service. In particular, it kept statistics on whether employees smile when helping customers.Cigna, a U.S. health insurance company in China, offers customers the option of applying for health insurance using a photograph instead of the usual written signature to reduce the risk of fraud.

Benefits of face recognition for business

A facial recognition camera is usually useful for:

  1. Enhancing security. Access control to buildings and premises. Authorization of users in systems and applications. Prevent unauthorized access. Recognize potential threats.
  2. Fast identification without user involvement. In contrast to other identification methods (entering a password or using a key), facial recognition does not require active user participation. This makes the process more convenient and intuitive.
  3. Automation of business processes. Accounting of working time and attendance of employees. Automation of payment for goods and services. Personalization of customer service. Improving marketing campaigns. Below, we will look at a specific example of a human recognition system that has had a positive impact on a business.
  4. Increased productivity, optimization of logistics and warehouse operations, automation of product quality control, and acceleration of document processing.

How to overcome limitations of face recognition technology

Today, face tracking works much more efficiently than it did in the nineties and even more so in the sixties. At first, we had the simplest systems, and they had to be handled manually. Now, we have moved on to advanced products with eye recognition and other biometric features. From 1993 to 2011, the error rate of automated facial recognition systems decreased 272 times.

But even modern systems have their limitations. They cannot completely replace other security measures. Here are a few reasons and the main disadvantages of facial recognition:

  • Recognition errors. Even the most advanced systems can make errors in facial recognition, let alone a simple free facial recognition app. Errors are often affected by poor lighting, poor image quality, or changes in a person's appearance (such as a change of hairstyle).
  • False positives. Facial recognition systems can misidentify a person, leading to false positives and wrongful detentions.
  • Overcoming the system. For example, the system can be fooled by wearing a hyper-realistic mask.
  • Privacy and ethics. The application of facial recognition technology can trigger privacy and ethical issues. The minimum required of a business is to obtain consent from the people whose faces are being recognized.

It is important to use facial recognition technology in conjunction with other security measures. For example:

  • Authentication by other biometrics (e.g., fingerprints)
  • Physical barriers
  • CCTV cameras
  • Trained personnel to detect suspicious behavior

Only the combined use of the various methods provides the most effective level of security possible.

When working with our clients, we help them determine how to solve a product problem with technology consulting or how to close a business need with product discovery. This includes determining quickly and efficiently whether you really need to implement facial recognition. We can also help you avoid common mistakes when integrating facial recognition technologies and get the most accurate results possible.

Free face recognition software

Facial recognition technologies are becoming increasingly trendy day by day. At the moment, big companies are creating several well-known solutions. You can use them to make your own face recognition app. 

Some of the best-known off-the-shelf solutions are:

  1. Amazon Rekognition. A cloud-based machine vision platform that provides a wide range of features for analyzing images and video. Allows developers to add object, face, text, scene, and action recognition capabilities to their applications. Available for up to 1,000 free minutes of video analysis per month.
  2. Microsoft Azure Face API. Provides face detection, analysis, and identification capabilities in images and video. There is a free plan available.
  3. Google Cloud Vision API. Includes facial recognition capabilities. Allows you to analyze and identify faces in images and video files. New users get $300 to use the service.
  4. FaceFirst. A real-time facial recognition platform that is widely used in various fields such as retail for security purposes.
  5. NEC NeoFace. A facial identification and authentication solution from NEC. It is used in security, access control, and other applications.
  6. Idemia Face Recognition. Face recognition technologies from one of the leading biometric solution providers in the world.

However, implementing facial recognition technology still presents a number of challenges:

  • Variability of conditions
  • Difficulties with accuracy
  • The need for a large database
  • Computational complexity

In addition, while such systems are becoming more common, their use raises questions about privacy and data protection.

COAX customers are fully informed about how personal data is used and stored. Users can view, edit, and delete their information. Compliance with all data protection statutes and policies is strictly enforced. We also use the strongest encryption methods to protect information from unauthorized access.

Implementing facial recognition with COAX

We helped our client to develop a timekeeping system (TRI system) using facial recognition technology. COAX’s team automated employee and contractor tracking processes by integrating Amazon Rekognition. 

The main problem the system solves is tracking the working hours of employees of different specialties, as they have different schedules. For example, plumbers work from 10 am to 7 pm, while electricians work from 9 am to 5 pm. In addition, their break times vary, and locations are constantly changing. Face recognition helps to automate this.

example of implementation facial recognition technology

The app has not only met the needs for solving the problems of employee time management but has also become an internal reporting tool for the administration, management, and finance departments. The TRI system now allows for more accurate tracking of employee and contractor time, resulting in better planning and optimized resource utilization.

Whether you need to integrate face recognition into your app or develop custom software from scratch, we’re here to help. COAX specialists have extensive software integration expertise and background working with face recognition technology. Drop us a line, and we will discuss how to meet your specific needs.


Is facial recognition accurate?

Facial recognition technology has improved significantly but its accuracy varies. Factors like image quality, lighting, pose, and environmental conditions can affect accuracy. Additionally, there are concerns about biases and ethical implications. While facial recognition can be highly accurate under optimal conditions, it's not infallible, and careful consideration is needed for responsible deployment.

Is face recognition safe?

Face recognition technology raises concerns about privacy, security, and potential biases. While it can enhance security and convenience, there are risks of unauthorized surveillance, data breaches, and misidentification.

Can facial recognition identify you if you’re wearing a mask?

Facial recognition technology typically struggles to identify individuals wearing masks, as the covered portion of the face obstructs key facial features used for recognition. However, some advanced systems may incorporate additional data sources or algorithms to assist in identifying masked individuals, but their effectiveness can vary.

What technology is used in face recognition?

Face recognition technology utilizes a combination of hardware components, such as cameras or sensors, and software algorithms, including deep learning and machine learning techniques, to analyze and identify facial features and patterns from images or video frames.

Where is facial recognition technology used?

Facial recognition technology is used in various industries and applications, including security and surveillance, access control, law enforcement, retail, banking, and entertainment.

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