Which scenes are used in face recognition ,the role of face recognition

- Oct 11, 2018-

Access control device access, vehicle access, intelligent ATM, computer access, program access, network access, etc.; · Security anti-terrorism alarm, boarding, stadium audience scanning, computer security, network security, etc.; · Monitoring park monitoring, Street monitoring, power grid monitoring, entrance monitoring, etc.; · smart card user authentication, etc.; · law enforcement suspect identification, fraud identification, etc.; · face database face search, face mark, face classification, etc.; · multimedia management face search, people Face video segmentation and splicing, etc.; • Human-computer interaction interactive games, active computing, etc.;


Access control device access, vehicle access, intelligent ATM, computer access, program access, network access, etc.; · Security anti-terrorism alarm, boarding, stadium audience scanning, computer security, network security, etc.; · Monitoring park monitoring, Street monitoring, power grid monitoring, entrance monitoring, etc.; · smart card user authentication, etc.; · law enforcement suspect identification, fraud identification, etc.; · face database face search, face mark, face classification, etc.; · multimedia management face search, people Face video segmentation and splicing, etc.; • Human-computer interaction interactive games, active computing, etc.;

Access control device access, vehicle access, intelligent ATM, computer access, program access, network access, etc.

·Safe anti-terrorism alarm, boarding, stadium audience scanning, computer security, network security, etc.; · Monitor park monitoring, street monitoring, power grid monitoring, entrance monitoring, etc.; · Smart card user authentication;

· Law enforcement suspect identification, fraud identification, etc.;

· Face database face search, face mark, face classification, etc.; · Multimedia management face search, face video segmentation and stitching, etc.; · Human-computer interaction interactive game, active calculation, etc.;

· Other face reconstruction, low bit rate pictures and video transmission;

Taking public security applications as an example, the public security departments often encounter unidentified personnel when investigating and handling cases, such as lost elderly people, children, suspects who refuse to give evidence, and unclaimed bodies. At this time, the traditional methods often cannot solve the problem. The face face is input into the system using the face retrieval system. The system automatically performs a search comparison in the massive population database, listing the first few similar personnel information. Then, through manual intervention, the system results are screened to obtain the true identity of the target.

A basic set of face recognition control system functions (1) face capture and tracking function

Face capture refers to detecting a portrait in a frame of an image or video stream and separating the portrait from the background and automatically saving it. Portrait tracking refers to the use of portrait capture technology to automatically track a specified portrait as it moves within the range captured by the camera.

Access control device access, vehicle access, intelligent ATM, computer access, program access, network access, etc.

·Safe anti-terrorism alarm, boarding, stadium audience scanning, computer security, network security, etc.; · Monitor park monitoring, street monitoring, power grid monitoring, entrance monitoring, etc.; · Smart card user authentication;

· Law enforcement suspect identification, fraud identification, etc.;

· Face database face search, face mark, face classification, etc.; · Multimedia management face search, face video segmentation and stitching, etc.; · Human-computer interaction interactive game, active calculation, etc.;

· Other face reconstruction, low bit rate pictures and video transmission;

Taking public security applications as an example, the public security departments often encounter unidentified personnel when investigating and handling cases, such as lost elderly people, children, suspects who refuse to give evidence, and unclaimed bodies. At this time, the traditional methods often cannot solve the problem. The face face is input into the system using the face retrieval system. The system automatically performs a search comparison in the massive population database, listing the first few similar personnel information. Then, through manual intervention, the system results are screened to obtain the true identity of the target.

A basic set of face recognition control system functions (1) face capture and tracking function

Face capture refers to detecting a portrait in a frame of an image or video stream and separating the portrait from the background and automatically saving it. Portrait tracking refers to the use of portrait capture technology to automatically track a specified portrait as it moves within the range captured by the camera.


(2) Face recognition calculation

The face recognition sub-verification and the search type are two comparison calculation modes. The verification method is to verify that the captured portrait or the designated portrait is compared with an already registered object in the database to determine whether it is the same person. Search-based comparison refers to searching from all the registered portraits in the database to find out whether a specified portrait exists.


(3) Modeling and Retrieval of Faces The portrait data registered in the library can be modeled to extract the features of the face, and the generated face template is saved in the database. When performing a face search, the specified portrait is modeled, and then compared with the template of the owner in the database, and a similar person list is listed according to the similar values of the comparison. Therefore, data has become a key factor in improving the performance of face recognition algorithms. Many applications pay more attention to the recognition performance under low false positive conditions. For example, the face acceptance needs to control the error acceptance rate at 0.00001.


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Therefore, future algorithm improvements will also focus on improving the recognition rate under low false positives. For security monitoring, it may need to be controlled within 0.00000001 (such as a registry of hundreds of thousands of people), and face recognition technology in the security field is more challenging. With the deep learning evolution, face recognition based on deep learning will make a breakthrough. All it needs is more and more data and samples. The more data and samples, the more it is trained, the easier it is to capture accurate results and give you accurate answers. Therefore, when a device of a face recognition system is fully introduced into the algorithm of deep learning, it is almost perfect to solve various long-term changes. Auto Watchdog Electronics Co., Ltd. is a high-tech research and development enterprise focusing on the "face recognition member system" and the passenger counter, car security products, adhering to the "intellectual wisdom, knowledge and everything" concept, since its inception We will fully promote the industrialization of computer vision technology, and actively carry out research on cutting-edge theory and advanced algorithm architecture by virtue of its first-mover advantage in deep domestic learning. The focus is on business strategy to promote the transformation of technology research and development into commercial practice. The existing product system covers intelligent transportation, smart communities, machine vision, intelligent security and other fields. Many products are in the leading position in the vertical segmentation field.