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So, how does biometrics identify personal characteristics?
We divide the biometric process into three steps: biometric data sampling, biometric extraction, and feature matching. The data sampling process is the process of collecting raw data from biometrics by various sensors. The biometric extraction process extracts information reflecting individual characteristics (usually a mathematical coding process) from the data collected by the sensors. It is the process of calculating the similarities between biological features and performing sorting and consistency judgments.
Biometrics technology mainly includes face recognition, fingerprint recognition, speech recognition, and iris recognition. Biometric technology has been developed in the last century. The fingerprint recognition technology has matured, but research on face recognition technology is still in its infancy. Face recognition can use existing photos or cameras to capture images from a distance, without special acquisition equipment, and the cost of the system is low. And automatic face recognition can complete identification and identification work without the parties' awareness, which is of great significance for anti-terrorism activities.
Second, face recognition technology overview
The purpose of face recognition is to let the computer have the function of identifying the identity through the features of the face. Face recognition based on facial features is mainly designed to face detection and recognition technology in complex scenes. It is a research direction based on high technology such as image understanding, pattern recognition, computer vision, statistics and artificial intelligence.
Face recognition technology, especially the maturity of comparison algorithms, has been available since 2014. Especially after 2015, domestic algorithms gradually tend to deepen the application of face recognition algorithms through deep learning (deeplearning) technology, and the recognition effect is more prominent than before. It can be said that the domestic leading facial recognition algorithms since 2016 are inseparable from the deep learning technology based on neural networks.
Third, the value of face recognition technology
Among different biometric methods, face recognition has its own special advantages and therefore has an important position in biometric recognition. In the typical application environment, the recognition accuracy of the face recognition technology can reach the level of the fingerprint recognition technology, and its user-friendliness is obviously higher than other several biometric recognition technologies. Its moderate price and superior performance are more recognized by users. At the same time, face recognition technology can be seamlessly integrated with the digital surveillance system, thus providing a good auditing and verification mechanism for the electronic eye transportation system and the safe city monitoring system, which greatly improves the system's security and ease of use.
Based on the actual application of fingerprint recognition technology, there are still several problems in the following aspects:
1. Experiments show that there is a possibility that fingerprints are copied by others, which undoubtedly reduces the security of the system;
2. The user is required to have a high degree of cooperation. In the fingerprint collection process, the fingerprint acquisition instrument needs to be in direct contact with the user, which may easily lead to a sense of infringement. As a result, the user's acceptance of the fingerprint recognition technology is reduced;
3. The fingerprint of some users is difficult to collect, and there is a problem of high system rejection rate;
The identification accuracy based on palm-shaped and iris recognition technology is higher than the fingerprint identification technology, but there are still problems that require the user to cooperate. It is difficult to use the special equipment, expensive, and other shortcomings to become mainstream products in the market.
Fourth, face recognition technology in various industries
At present, face recognition technology has been widely used in various industries, such as: financial VIP customer visitor system, remote account opening system; building face access control, face attendance system; Internet mobile payment terminal, dating, dating terminal APP system; industrial / civil intelligence Robotic products etc.
Taking civilian intelligent robots as an example, Zhejiang Dahua recently launched a childcare robot product (as shown in Fig. 1) to implement face detection, tracking, and recognition technologies through robots. The default recipient "master" is set according to simple face settings and voice mail. After the robot starts, it can recognize that different faces call different names, and can also better track the face according to the position. The robot can always face the user. The direction of rotation, full of sense of science and technology and interaction. 
The most widely used and advanced face recognition technology is the * field. At present, through the combination of face recognition systems and local policing applications, many areas* have created a number of business applications and special rectification activities.
Fifth, the status of the application of face recognition in the industry * Actual demand can be roughly divided into the vehicle, the person, the object of the requirements of the judge. Nowadays, with the development of technology, the industry has basically solved such basic requirements as the license plate inspection and control, and has turned to the need to solve the deep application requirements of the upper-layer tactics such as fake decks, first-time entry into the city, and vehicle feature recognition; the object is currently limited to the investigation of legacy. Initial applications such as loss, movement, or lack of movement still lack the depth and wisdom of the application of objects; the demand for people is even wider, from the color, direction and speed of people’s clothing to the requirements of appearance, expression, and gait, of which the most popular and the most developed. The outlook is face recognition applications. In recent years, face recognition has been applied "quietly" in the industry and has been successfully systemized, applied, and actualized.
* Face recognition applications are generally divided into three types: First, to achieve real-time warning of "concerned" personnel, continuously collect video through the video surveillance front-end, and the back-end uses face recognition technology as the core to conduct "concern" personnel comparison and adoption. The police inform the police on the spot about the target arrest; the second is the verification of the identity of the personnel in the event and afterwards. The back-end system needs to “tag†the second-generation ID card and upload the target face picture through the smart terminal or system. , The back end looks for matching ID pictures from a large number of second-generation identity card libraries, and gives associated identity information. Third, it implements personnel identity verification, that is, the use of face recognition technology to achieve the hand-held ID card personnel and ID cards. Compare, conduct a review of the human certificate.
Taking a city in Fujian as an example, local* captures local red light pedestrians through face recognition and compares the captured photos with the resident population of the province, and conducts daily comparisons to the WeChat public account. The demonstration effectively curbs pedestrians' violations of the red light and implements the nation’s first routine “Human Red Light Face Capture Exposure System†(shown in Figure 2). 
VI. Future of Face Recognition Technology
Nowadays, the popularity of second-generation permits in China has provided digital photo data for the identity of about 1.4 billion people in China. Data show that 12 out of 1.4 billion people in the country are almost exactly the same as you, and data applications have gradually exceeded current human identification. At the same time, the millions of surveillance cameras that are connected to the safe city network also generate huge amounts of data every day. These data have a large amount of application value to be tapped. All indications indicate that *In order to enter the era of big data applications, face recognition technology wants to mature in the * industry, some traditional technology bottlenecks appear gradually, response speed is getting slower and slower, and some application scenarios are completely unsustainable.
* Big data application background brings opportunities for face recognition applications, and it also raises higher requirements for face recognition technology. Although the performance of face recognition technology has been greatly improved, it is still one of the most difficult problems in the field of pattern recognition and computer vision. How to use face recognition technology to use these massive photo data to improve the overall management level of information technology has become an important issue before us.
1. Face recognition requires larger capacity and higher accuracy. Currently, various applications such as household management, immigration, identification of criminal suspects, etc., need to be identified and processed based on national face data. With a database capacity of hundreds of millions or billions, there is a large number of matching requests and the models are not uniform. It is a challenging task to rapidly and accurately identify identities from databases of this size.
2. System input extends from purely static images to dynamic video In recent years, the government agencies throughout the country have been vigorously developing video surveillance systems. According to incomplete statistics, the amount of data that needs to be stored each year is as high as 3.3 EB, combined with video surveillance and face recognition. The rapid identification and real-time deployment of criminal suspects is an important way to improve the efficiency of video surveillance. However, face recognition video surveillance is faced with a variety of factors such as light, angle, pose, and occlusion, resulting in an increase in intra-class gaps and narrowing of gaps between classes. This has brought great challenges to face recognition combined with video surveillance. challenge.
3. The sources of images are more extensive, and the image quality is greatly different. Through the construction of the "Golden Shield Project", eight resource pools have been successfully built, and massive amounts of data have been accumulated, laying the foundation for the widespread application of face recognition technology in public safety. However, due to the lack of a unified construction standard, the quality of face data in various types of business varies widely, making it difficult for face recognition applications.
In the traditional methods of investigation, photo comparisons can only be carried out manually. The workload is huge, slow, inefficient and unable to adapt to business needs. The high-speed automatic recognition capability of the face recognition system can largely free the security department from the “human sea tactics†of the past and greatly increase the security protection level of the entire country and society, thus achieving deterrence and punishment. Criminals, safeguarding social stability, and safeguarding national security.
Conclusion <br> <br> With the rapid development of face recognition technology, standards have gradually expanded. The Security Biometrics Standards Committee (SAC/TC100/SC2) has also developed a number of face intelligence standards in recent years, such as GBT31488-2015 and GAT1093-2013. However, the face recognition technology research has not yet been completed. In the current business, face recognition technology has been covered including exit and entry management, household registration check, and witness verification. However, this is still limited to the situation where the image quality is controllable. Under the more widely used video surveillance scenes, the gestures, expressions, and illumination of people are all greatly changed.
To better adapt to the * scenario, relying solely on a technology, a product is still far from meeting the needs, and an integrated solution provider must be sought to adapt the * scenario requirements from an overall perspective to meet different business needs. . The urgent and extensive needs of face recognition technology in the field will also further promote the research and application of face intelligence.
Analysis of Face Recognition Technology in Application
First, biometric identification technology <br> <br> biometrics personal identification is performed by a computer using the inherent physical characteristics of the human body or behavioral characteristics. Physiological characteristics are born with congenital nature; behavioral characteristics are habitual, mostly acquired. We collectively refer to physical and behavioral characteristics as biological characteristics. Commonly used biometrics include: fingerprints, palm prints, irises, retinas, faces, sounds, handwritings, etc.
Figure 1 Parenting robot products
Figure 2 face capture exposure system