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How does machine learning change facial recognition technology?

  • Author:HFSecurity
  • Release on:2020-12-15

With the perfect storm of digital transformation, we are entering a new era of fast security certification. The fusion of artificial intelligence and biometric technology is part of this transformation, and like many technological breakthroughs, this transformation does not depend solely on the advancement of a single technology.

The realization of face recognition has had many iterations since its origin in the 1960s, when it was manually implemented using a RAND tablet (graphic computing device). This technology has gradually improved in the last century. However, due to the breakthrough of deep machine learning in the early 2010s, it is possible to adopt facial recognition on a large scale. In this article, we will try to explain how machine learning has changed facial recognition technology and its impact on robust authentication systems.

What is face recognition?

Facial recognition is a method of using technology to recognize human faces. Facial recognition systems use facial biometrics to map facial features using images or pictures in video frames. It compares the information in the database to find an exact match. Face recognition helps to verify someone’s identity and is a widely used security measure.

Facial Recognition Device

By 2022, the facial recognition market is expected to grow to 7.7 billion U.S. dollars, because the technology has various commercial applications. From airport security to healthcare and customer identity verification, facial recognition is now widely used worldwide.

What role does machine learning play in the face recognition mechanism?
Deep machine learning or deep neural networks are about computer programs that learn by themselves. It is called "neural network" or "neural network" because the technology is inspired by the properties of the human brain to convert data into information. It is a variant of the more general machine learning concept, which in turn is part of a broader concept called artificial intelligence.

Through deep machine learning, algorithms can provide training data and provide results. But between input and output, the algorithm interprets the signal in multiple levels, namely (training data). For each new level, the level of abstraction will increase in different levels

Suppose you want to build a deep neural network that can distinguish different faces or determine which faces are the same. The training data should be a lot of facial images. At least in theory, the larger the data set, the more accurate the network.

The computer cannot "see" the face in the image, but instead represents multiple values ​​of different pixels. With pixels as the background, deep neural networks learn to find patterns. For each layer passed in the network, some patterns become more interesting (the signal between "neurons" in the network is stronger), while other patterns do not complain (the signal is weaker). During the training process, the "weights" of various signals are constantly changing to produce better and better desired results.

The first time, the second time, and the hundredth time the algorithm performs this process, the results are usually not so good, but in the end, the network can achieve impressive results. It can be said that from the original pixel value to the classification of different faces, the network has learned to abstract and generalize.

HFSecurity Face Recognition Device

However, when we use terms such as induction, this may not be what we humans think of. On the contrary, the network has developed some indicators for each face. If the pre-trained network provides a new image on the face, the network can match its measurement with faces on other images. If the network generates roughly the same value for different images, then the two images may be the same person.

Why it is called deep machine learning is because this deep machine learning model can use multiple (up to 100) levels. But so far, humans have not been able to know how computer programs find their own patterns, and this pattern can also run in different layers.ers.

Although the algorithm was developed and improved as it is, there are two reasons behind the breakthrough of deep neural networks: access to large data sets and cheap computing power, especially in the form of graphics cards most commonly associated with computer games.

It can also be remembered that the above method for classification purposes is only one of many methods, but is commonly used.

Anti-spoofing technology for face recognition

Although the fusion of machine learning algorithms and facial recognition algorithms can make the operating environment more accurate and faster, there is still a necessary condition to be able to learn facial recognition authentication and anti-spoofing capabilities. This innovative technology shows great promise and has the potential to change the way we access sensitive information.

Although face recognition is promising, it does have some shortcomings. By using paper images from the Internet, simple facial recognition systems can be easily deceived. These spoofing attacks may lead to the leakage of sensitive data. This is where an anti-spoofing system is needed. Face anti-spoofing technology helps prevent fraud, reduce theft and protect sensitive information.

Demonstration attacks are the most common spoofing attacks used to spoof facial recognition systems. The presentation attack aims to destroy facial authentication software by presenting facial artifacts. Popular facial artifacts include printed photos, playback videos, 2D and 3D masks.

Two types of anti-spoofing technologies based on AI have emerged, which can respectively enable detection capabilities and the ability to combat facial spoofing attacks.. Based on functions such as 3D depth perception, dynamic detection and micro-expression analysis, our deep learning-based facial authentication system can accurately analyze facial data and identify almost all types of deception attacks. Shufti Pro detected 42 different spoofing attacks in 2019. In these 3D mask attacks, the number of attacks is high-almost 30%.

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A presentation attack detection algorithm based on machine learning is used to automatically identify these artifacts to improve the security and reliability of the biometric system.

The face verification system based on machine learning relies on 3D liveness detection to successfully detect spoofing attacks, including 3D photo masks, deep forgeries, facial deformation attacks, fake and photoshopped pictures. Activity detection can verify whether users are present or using photos to deceive the system.

What are your expectations for facial recognition in the future?

Human face has become a perfect means of identity verification and will have a greater impact on digital transformation in the future. By using facial identifiers, we have been able to open online accounts, make online payments, unlock smartphones, control through airport inspections or access medical records in the healthcare department.

Generally speaking, facial biometric technology has broad potential in the following four categories: law enforcement and security, online marketing and retail sector, health sector, and social media platforms. AI-based facial recognition technology may become mainstream in the future.

One of the future effects of technology is the recognition of facial expressions. Detecting emotions with the help of technology is a difficult task, but machine learning algorithms have the potential to automate this task. By recognizing facial expressions, companies will be able to process images and videos in real time for better monitoring and prediction, thereby saving cost and time.

Although it is difficult to predict the future facial recognition technology with the rapid development and adoption of technology, it will be more widely adopted globally with more complex functions.

If you have any inquiries about biometrics, you can contact us.

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