Face recognition application background The rapid development of the economy has brought about frequent movements of people between different regions. As a result, the demand for the safety management of public personnel has increased rapidly. Every year, criminal cases and public security cases have risen year by year, and many people involved are at large. There are also a larger number of thieves who are difficult to arrest. According to incomplete statistics, at present, there are about 500,000 people in public service. Public security in various places needs new technical means to assist in the technical investigation of their cases and provide security for key areas. On the other hand, at present, Ping An City has basically completed the construction of networking and high-definition, and is moving towards a new round of development represented by practical applications and cloud services. The existing monitoring system generates massive amounts of network HD video data every day. Among them, there is a large amount of available face information, and the current utilization of these face information is not high, the supporting tools are simple, and even rely on manual methods. Therefore, in order to achieve rapid identification of the identity of people in massive video, face recognition technology is undoubtedly the best choice. As an emerging security intelligent product, face recognition began in the 1960s. When computers appeared in the 1990s, face recognition entered the real stage of automatic machine identification. At present, in the field of security monitoring, face recognition is mainly based on face recognition of visible light images. The human face is innate with other biological characteristics of the human body (fingerprint, iris, etc.). Although the ambient light and the resolution of the face have an influence on the recognition result, it is non-mandatory and concealed compared to other feature recognition. Features such as friendliness and high concurrency, therefore, face recognition products have incomparable advantages for applications in an open public environment. At present, in the face recognition products on the market, the use of static face recognition products is relatively extensive and mature. In the scenes of customs clearance, finance, telecommunications, notarization, etc., it is necessary to verify the consistency of people and documents. good performance. Especially in the financial industry, due to the need for identity verification and the promotion of VTM in banking business, customers generally have to verify and verify the identity of customers in the process of handling business or self-service business. In actual application, the system obtains the identity document of the customer. The face photos and the pictures of the on-site customers are used for face recognition comparison, and the verification of the identity of the person is completed, which greatly improves the efficiency of financial business operations. The application of dynamic face recognition is currently in the early stage, and gradually began to be promoted in the fields of transportation, public security, buildings, and communities. Due to the non-coordination of the recognition target, the dynamic face recognition application is slightly more complicated than the static face. The high accuracy and timeliness of face recognition is the premise of business application. It requires professional team development and deployment to achieve satisfactory results. . Face recognition technology and application mode are two wings, service is the main body Face recognition algorithm The current face recognition system is a process of face image acquisition, preprocessing, feature extraction, and matching recognition. At present, there are many algorithms for face recognition, and the face recognition algorithm based on neural network is highly praised in the industry. The neural network algorithm is a complex system that is inspired by the human nervous system and uses a large number of simple processing units to interconnect. It imitates the human cognitive system. Through the learning process, it is difficult to realize the rules and rules of face recognition that are difficult to achieve by other methods. Sexual expression. After a large number of face positive and negative sample data training, the algorithm has obvious advantages in accuracy, fault tolerance and robustness compared with other algorithms. It is an efficient learning algorithm and is very suitable for solving face recognition. The problem. Only by mastering the core algorithm of face recognition can an enterprise develop a face recognition product that meets the application requirements. At present, only a few large manufacturers and research institutions in the industry have launched face recognition products. For example: Jiadu Technology, Hikvision, Dahua, etc. Taking Jiadu Technology as an example, its face core algorithm adds a deep learning mechanism based on the neural network, continuously monitors and fine-tunes the learning network to obtain better recognition results. Mixing old and new projects, multiple application models are the foundation At present, the face recognition system in the security system is mainly based on a system for detecting, recognizing, alarming, and querying a dynamic face in a surveillance video, generally including: a face collection service module, a face real-time recognition service module, Face retrieval service module, application service module. It can provide face capture, 1: N dynamic face recognition, face search query and other face services in real time and afterwards. For the face acquisition module, there are currently two implementation methods. The first type uses a camera with a face capture function to directly collect face information. The camera directly captures the face image from its own video image and transmits it to the face image. Background recognition and storage. This mode is suitable for the face recognition application in the newly constructed monitoring project. The face capture camera can be reused, which is used for both daily monitoring and face capture without increasing the cost. The second type uses an ordinary network camera to cooperate with the face to capture the host, and the face capture host captures the face image information from the video stream of the camera and transmits the image information to the recognition module. This mode is suitable for secondary transformation based on the original monitoring system. It can increase the face recognition application service without purchasing and installing a new face capture camera, saving customer cost and simplifying deployment conditions. At present, the face recognition module of Jiadu Technology's face recognition system integrates the above two schemes, fully complementing each other's advantages, and providing users with optimal solutions according to the actual situation of the project. Service is the key to grasp customer needs Face recognition in security systems is more than just a subsystem for face detection and recognition of alarms. For users, what is needed is a face recognition service that can solve one or several types of problems, through face recognition services. Application, which can optimize or enhance its business process and improve business efficiency, is a complete set of face recognition service system. It is not just a discrete service function for the centralized control of key personnel. For example, after a case occurs, in order to find a suspect, through the face capture, face check, face search summary to assist the investigation of the case, through the face trajectory to provide clues to solve the case, after locating the suspect, through the face control alarm To provide information for the arrest of suspects. In addition, in the wave of safe city construction and smart city construction, even a small city monitoring system generates a large amount of face data every day. The traditional storage mode is difficult to use for secondary use, and it is necessary to perform face big data. Structured cloud identification storage solves the serious problem of data scale expansion and performance degradation, making large and discrete face data become organic as a whole. Of course, face recognition products are closely related to the installation and deployment of cameras due to their own technical conditions and the actual application. The same face recognition products may be brought to customers in different application scenarios. A completely different experience. Since the personnel in the monitoring system screen are in a free state of activity, the target is prone to problems such as motion blur, mutual occlusion, and bowing of the face, which makes it difficult and inevitable for the detection and recognition of the face, and is installed in response to these problems. In the process of deployment, measures such as on-site fill light, high frame rate, and telephoto reduction of the depression angle are generally used to effectively alleviate the above problems. Face recognition provides protection for practical applications and smart cities As an important identification mark, face recognition plays an important role in the police service of public security organs. In daily patrols, household registration surveys, immigration management, and criminal investigations, the identity of relevant personnel is verified by identifying faces. At the same time, in the criminal case investigation video, a lot of time and police force was wasted due to a large number of video recordings. The structured cloud recognition storage management of the face recognition system can improve the practical ability of the public security by combining the efficiency of such practical applications with other case clues. In the construction of smart cities, focusing on the structured storage and analysis of information, the structured cloud recognition storage of human faces is one of the basic data for building the whole smart city, and it is part of the smart city cloud storage system. Through the high-speed data transmission chain and structured data filtering of smart cities, the collision between face big data and other big data in smart cities can highlight the value of face recognition. Face Cloud Recognition - A New Application for Face Recognition The face recognition cloud service is a cloud identification server rental service for enterprise users and high-end users. The cloud recognition service is deployed in the backbone data center of the Internet, and can independently provide services such as face recognition calculation, storage, and data backup. The traditional sales and application mode of the face recognition system, when users use the traditional face recognition server, due to many factors such as cost, operator, daily maintenance, upgrade, etc., face various difficult problems, and with the flexibility The introduction of the face recognition cloud computing server effectively solved this problem. The face cloud identification service is mainly based on the host service lease and the virtual dedicated server mode, and the client does not need to purchase the host device, and any PC and mobile terminal in the user network node can obtain the face recognition provided by the face recognition service. special service. Users can terminate the use of the service according to the needs of the business, and no longer use the usage fee. Face recognition application prospects and development trends With the rapid development of security technology, the networked and high-definition monitoring provides a hardware foundation for face recognition. The development of cloud computing and cloud storage provides technical support for face data, and the demand for face recognition is becoming more mature and clear. 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