The artificial intelligence platform should be a snowball closed loop that is continuously cycled through data, technology and products. Take Google "search engine" as an example, the product is a web page search engine, the technology is an algorithm for realizing page rank, and the data is the ranking of the page generated by the user click. In the security field, taking the defensive technology “deep learning engine†as an example, if the product is an intelligent visual cloud platform, the technology is a deep learning algorithm, and the data is labeled image data. Then, the artificial intelligence platform is a product polished by data and technology, and new data is generated in the process of product application, and the process of iterating and deepening the learning is continued. That is, products, technologies and data form a closed loop of super artificial intelligence technology. The ultimate goal is to form the functional effects of machine vision, giving the machine a pair of eye-catching eyes, so that it can "see" and "think" like a human. The machine has a human-like visual function to realize various functions of detection, judgment, recognition, measurement, and the like.
Productized commercial application of face recognition
Face recognition is a representative technology of current artificial intelligence, and its ability to productize and commercialize applications is critical. In terms of accuracy and security, with the contempt of technology face recognition technology as reference, the accuracy rate is less than one in 100,000, and the pass rate is over 95%. In terms of security level, it is more secure than fingerprint recognition, and does not require special hardware support requirements, and is more easily promoted.
In the field of technical application of face recognition, specific business functions have been realized in commercial buildings, helping merchants to achieve accurate analysis of passenger flow from “statistics†to “attributes†to “behaviorâ€, and to obtain more by machine vision analysis. The multi-line data dimension uses big data analysis to guide the precise marketing of merchants, and promotes the transformation of the retail industry from the same products and services to all people, to the same products and different services to specific people.
At the same time, the use of face recognition technology to bring a more intelligent management experience to enterprises and parks. For example, through the face recognition technology productization, the traditional scenes such as face recognition gates, access control, and conference sign-in bring management efficiency and convenience.
Face recognition pan security industry application
The traditional security industry is dominated by video surveillance. There are many opportunities for the application of structured data in massive unstructured video data. Desperate technology hopes to empower the traditional security industry through artificial intelligence technology, allowing the machine to understand the world rather than the chaotic video stream, thus enabling the traditional industry to activate more value.
Based on the timeline analysis of face recognition development, face recognition technology has experienced the laboratory algorithm stage, software SDK stage, single product stage, solution stage, and integrated platform stage. At present, many enterprises are in the software SDK stage and single product stage. This stage is actually a DEMO stage, defiance of technology in the single product stage, solution stage and integrated platform stage. Currently, the application fields are pan-finance, pan-commercial and pan-security. Among them, the pan-security application has two applications during the G20 summit in Hangzhou. One is at the front desk of the hotel, and the identity of the ID card is matched by the face of the resident. The other is to identify the fugitive in the sea by the personnel bay of the subway line. And suspicious molecules.
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