Brief analysis of face recognition technology
Face recognition is a biometric technology based on human face feature information. It uses a camera or a camera to capture an image or video stream containing a human face, and automatically detects and tracks the face in the image, and performs related processing on the detected face. Face recognition needs to accumulate a large amount of data related to face images to validate the algorithm and continuously improve the recognition accuracy.
Existing face recognition systems can achieve satisfactory recognition results under ideal conditions of user collaboration and acquisition. However, the recognition rate of existing systems will suddenly decrease under the condition of non-cooperation of users and less than ideal conditions of acquisition. For example, when comparing a face with a face stored in the system, such as shaving, changing hairstyle, wearing more glasses, or changing expressions may cause the comparison to fail.
Face recognition technology is one of the widely used regional feature analysis algorithms in biometric technology, which integrates computer image processing technology and biostatistics principles in one, using computer image processing technology to extract human image feature points from video and using biostatistics principles to analyze and establish mathematical models, i.e. face feature templates. The completed face feature template is used to analyze the features with the face image of the subject, and a similarity value is given according to the result of the analysis. This value is used to determine if the person is the same.
The advantage of face recognition lies in its naturalness and the absence of perceived features of the tested individual. By naturalness, we mean that the recognition method is the same as the biometric features used by humans in individual identification. For example, face recognition distinguishes individuals by observing and comparing faces and distinguishing identities, plus natural recognition as well as voice recognition, body type recognition, etc. Fingerprint recognition, iris recognition, etc. are unnatural because humans or other organisms do not distinguish individuals by such biometric features. Undetected features are also important in recognition methods, which makes them inoffensive and less likely to be deceptive, as it is less likely to attract attention. Face recognition has such characteristics that it uses visible light exclusively to acquire information about face images, but unlike fingerprint recognition or iris recognition, which require the use of electronic pressure sensors to capture fingerprints, or infrared light to capture iris images, these particular capture methods are easily detected and therefore more easily spoofed by disguises.
Face recognition is considered to be one of the difficult research topics in the field of biometric recognition and even artificial intelligence. The difficulty of face recognition is mainly caused by the characteristics of the face as a biometric feature, where the differences between different individuals are very small and the structure of the face is similar, even the structure and shape of the face organs are very similar, and this feature is beneficial to the use of face localization but not to face recognition. The shape of the human face is very unstable, and people can produce many expressions by the changes of the face, and the visual image of the face changes a lot on different observation angles. In addition, face recognition is affected by many factors, such as lighting conditions, mask of the face, age, etc.
In face recognition, one type of variation should be magnified to serve as a criterion to distinguish individuals, while another type of variation should be eliminated because they can represent the same individual. One type of variation is usually referred to as interclass variation and the other as intraclass variation. For faces, intra-class variation tends to be greater than inter-class variation, which makes it difficult to distinguish individuals by inter-class variation with the interference of intra-class variation. Face recognition is mainly used for identity recognition. Due to the rapid popularity of video surveillance, many video surveillance applications urgently need a long-distance, non-cooperative state fast identification technology to quickly confirm the identity of remote personnel and achieve intelligent warning. Face recognition technology is undoubtedly a good choice. Using fast face detection technology can find faces from surveillance video images in real time and compare with face database in real time, thus realizing fast identification.
Advantages of face recognition technology
As an emerging biometric identification technology (Biometrics), face recognition technology has unique advantages in application compared with iris recognition, fingerprint scanning, palm scanning and other technologies: easy to use, high user acceptance Face recognition technology uses a universal camera as a recognition information acquisition device to complete the recognition process in a non-contact manner without the recognition object noticing Recognition process. Intuitive outstanding face recognition technology is based on the human face image, and the human face is undoubtedly the most intuitive source of information that can be discriminated by the naked eye, which is convenient for manual confirmation and audit, and “judging people by their appearance” is in line with the law of human cognition. High recognition accuracy and speed Compared with other biometric technologies, the recognition accuracy of face recognition technology is at a high level, and the false recognition rate and rejection rate are low.
In applications with high security requirements, face recognition technology requires the recognition object to be physically present at the recognition site, making it difficult for others to counterfeit. The unique active discrimination ability of face recognition technology ensures that others cannot cheat the recognition system with inactive photos, puppets or wax figures. This is difficult to do with biometric technologies such as fingerprints. For example, the identity of a legitimate user can be impersonated by a severed finger of a legitimate user without the identification system being able to detect it. The equipment used by face recognition technology is general PC, camera and other conventional equipment, because the computer, closed-circuit television monitoring system and so on have been widely used, so for most users to use face recognition technology without adding a lot of special equipment, thus not only protecting the user’s original investment but also expanding the function of the user’s existing equipment, to meet the user’s security needs.
The basis information is easy to obtain face recognition technology is based on the face photo or the face image taken in real time, so it is undoubtedly the easiest to obtain. Low cost, easy to promote the use of face recognition technology because the use of conventional general equipment, the price are in the general user acceptable range, compared with other biometric technology, face recognition products have a very high performance to price ratio. In summary, face recognition technology is a high-precision, easy to use, high stability, difficult to counterfeit, cost-effective biometric identification technology, with extremely broad market application prospects.
Disadvantages of face recognition technology
Face recognition is considered to be one of the most difficult research topics in the field of biometric recognition and even in the field of artificial intelligence. The difficulties of facial recognition are mainly brought about by the characteristics of the face as a biometric feature. Similarity is not very different between different individuals, all faces have similar structures, and even the structural appearance of facial organs is similar. Such characteristics are advantageous for localization using the face, but unfavorable for differentiating human individuals using the face. In addition, facial recognition is affected by many factors such as lighting conditions (e.g., day and night, indoor and outdoor, etc.), many coverings of the face (e.g., masks, sunglasses, hair, beard, etc.), and age.
In facial recognition, the first class of variations is supposed to be enlarged and used as a criterion to distinguish individuals, while the second class of variations should be eliminated because they can represent the same individual. The first type of variation is usually referred to as interclass variation, while the second type of variation is referred to as intraclass variation. For faces, intra-class variation is often greater than inter-class variation, thus making it exceptionally difficult to distinguish individuals using inter-class variation when disturbed by intra-class variation.
Process of face recognition technology
Face recognition system mainly includes four components, which are: face image acquisition and detection, face image pre-processing, face image feature extraction, and matching and recognition.
1. Face image acquisition:
Different face images can be captured through the camera lens, such as static images, dynamic images, different positions, different expressions and other aspects can be well captured. When the user is within the shooting range of the acquisition device, the acquisition device will automatically search and shoot the user’s face image. Face detection: Face detection is mainly used in practice for pre-processing of face recognition, i.e., to accurately calibrate the position and size of a face in an image. The face image contains rich pattern features, such as histogram features, color features, template features, structure features and Haar features. Face detection is to pick out the useful information from these and use these features to achieve face detection.
2. Face image pre-processing:
Image pre-processing for faces is the process of processing images based on face detection results and eventually serving for feature extraction. The original image acquired by the system is often not directly usable due to various conditions and random interference, and must be pre-processed at an early stage of image processing, such as grayscale correction, noise filtering and other image pre-processing. For face images, the pre-processing process mainly includes light compensation, grayscale transformation, histogram equalization, normalization, geometric correction, filtering and sharpening of face images. 3.
Face image feature extraction: The features that can be used in face recognition system are usually divided into visual features, pixel statistical features, face image transformation coefficient features, face image algebraic features and so on. Face feature extraction is performed for certain features of the face. Face feature extraction, also known as face characterization, is the process of feature modeling of faces. The methods of face feature extraction are summarized into two categories: one is knowledge-based characterization methods; the other is algebraic feature-based or statistical learning characterization methods.
Face image matching and recognition: The extracted feature data of the face image is searched and matched with the feature template stored in the database by setting a threshold value, and when the similarity exceeds this threshold value, the matched result is output. Face recognition is to compare the features of the face to be recognized with the obtained face feature template, and to judge the identity information of the face according to the degree of similarity. This process is divided into two categories: confirmation, which is a one-to-one image comparison process, and recognition, which is a one-to-many image matching comparison process.
Application areas of face recognition technology
With the in-depth research of intelligent technology, the term face recognition appears in our life more and more frequently. Face recognition technology, a biometric technology based on the information of human face features for identity recognition. A series of related technologies that use a camera or a camera to capture images or video streams containing human faces and automatically detect and track human faces in the images, and then perform face recognition on the detected faces, are often also called portrait recognition and facial recognition. So how many people understand how wide the application range of face recognition technology is? Today, Lupin Chief takes you to understand the following.
Public Security Criminal Investigation and Crime Solving
Search for the existence of key population basic information in the database by querying the target portrait data. For example, installing systems at airports or stations to catch criminals at large.
Control de Acceso Facial.
Secure areas can be identified by face recognition of those trying to enter. Face recognition systems can be used for corporate and residential security and control access control face recognition, such as face recognition access control attendance system, face recognition security doors, etc. Face recognition access control is based on advanced face recognition technology, combined with mature ID card and fingerprint identification technology and the launch of a safe and practical access control products. The product adopts a split design, the collection of face, fingerprint and ID card information and biological information recognition and access control inside and outside the separation, high practicality, safety and reliability. The system adopts network information encryption transmission, supports remote control and management, and can be widely used in banks, military, public prosecution and law, intelligent buildings and other key areas of access control security control.
Camera surveillance system
It can be used to monitor crowds in public places such as airports, stadiums and supermarkets, for example, installing surveillance systems in airports to prevent terrorists from boarding. For example, ATMs in banks, where users’ cards and passwords are stolen, can be used by others to withdraw cash fraudulently. The simultaneous application of face recognition will prevent this from happening.
Using face recognition to assist credit card network payments to prevent non-credit card owners from using credit cards, etc. Such as computer login, e-government and e-commerce. In e-commerce transactions are all done online, and many approval processes in e-government have been moved online. And currently, authorization for transactions or approvals is done by passwords. If the password is stolen, there is no guarantee of security. If biometric features are used, it is possible to achieve the unification of the digital identity and the real identity of the person concerned on the Internet. Thus, the reliability of e-commerce and e-government systems will be greatly increased.
Credit card network payment
Use face recognition to assist credit card network payments to prevent non-credit card owners from using credit cards, etc.
such as electronic passports and ID cards. This is perhaps the largest application of the future. ICAO has made it mandatory for its 118 member countries to use machine-readable passports from April 1, 2010, with face recognition being the first mode of identification, and the requirement has become an international standard. The United States already requires countries with which it has visa waiver agreements to use an electronic passport system that incorporates biometric features such as facial fingerprints by October 26, 2006, and by the end of 2006 more than 50 countries had implemented such a system.
The U.S. Transportation Security Administration (TSA) plans to roll out a biometric-based universal domestic travel document throughout the United States. Many European countries are planning or implementing similar programs to identify and manage traveler activities with biometric documents. Crowd surveillance can be conducted in public places such as airports, stadiums, and supermarkets, for example, by installing surveillance systems at airports to prevent terrorists from boarding. For example, ATMs in banks, where users’ cards and passwords are stolen, can be used by others to withdraw cash fraudulently. Applying face recognition at the same time will avoid this situation. Searching for the presence of basic information of key population in the database by querying the target portrait data. For example, installing systems at airports or stations to catch criminals at large.
Face autofocus and smiley face shutter technology: First of all, face capture. It determines according to the parts of the human head, first determines the head, then judges the head features such as eyes and mouth, and confirms that it is a human face by comparing the feature library to complete face capture. Then the face is used as the focus for autofocus, which can greatly improve the clarity of the photos taken. The smiley shutter technology is based on face recognition, completing the face capture, and then starts to judge the degree of upward curvature of the mouth and the degree of downward curvature of the eyes to determine if it is a smile. All the above capturing and comparing are done in the case of comparing feature libraries, so feature libraries are the basis, which have various typical facial and smiley face feature data.
Face recognition technology is widely used in daily life, such as camera shooting, picture comparison, etc. Especially in the past two years, dating programs are in full swing, among which the best couple face segment in Zhejiang TV’s Love Connection uses face comparison technology to test the similarity of male and female main characters’ faces. With the rise of mobile Internet, some developers of face recognition technology have applied the technology to the entertainment field, such as the application of happy star face, which calculates the similarity between the main character and the star in the photo based on the contour, skin color, texture, texture, color, lighting and other characteristics of the face.
July 2013. A Finnish company launched the world’s first “face swipe” payment system. When checking out, consumers simply face a camera on the POS screen at the checkout counter, and the system automatically takes a photo, scans the consumer’s face, and then clicks on the touch screen to confirm the transaction after the identity information is displayed. No credit card, wallet or cell phone is required. The entire transaction process does not exceed 5 seconds. However, some people believe that “this time, usually just enough to take out your wallet”.