Application of face recognition algorithm in ptksai equipment
The popularity of the face recognition application market has brought development opportunities to many start-up companies, and many face recognition companies have emerged in the market at that time. This phenomenon, while many industry users in need are facing more cooperative choices, there are also many puzzles: every Enterprise says it has a core face recognition algorithm, what is a good face recognition algorithm? Is there a measure?
A large number of practical databases are very important for optimizing face recognition algorithms (
, Is to obtain the user's face image through video capture equipment, and then use the core algorithm to calculate and analyze the facial features, face shape and angle of the user's face, then compare with the existing templates in your own database, * determine the true identity of the user.
In fact, it is not difficult to see from the definition of face recognition that face recognition technology is different from other science and technology and has distinct characteristics. Light, age changes, angles, etc. will all affect the * final effect of face recognition, in order to deal with such external factors, it is required that the face recognition algorithm needs to continuously carry out autonomous learning, always in a process of dynamic evolution, and continuously accumulate rich face databases through practice, this requires the face recognition algorithm developed by enterprises to realize evolution in a large number of practical cases. It's like what is often said in the Internet concept 【First mover advantage]
The characteristic of the face recognition algorithm determines that the longer the time, the more the accumulation, the face recognition algorithm is implemented 【Evolution]The more you imagine.
The stability of the algorithm can also be improved in this evolution process, which is crucial for enterprise applications.
The practical cases of large-scale application are not available to all enterprises, and only a few domestic face recognition giants have such strength.
Taking Tiancheng Shengye as an example, the face recognition algorithm has been developed for 8 years, and the original Dynamic Template fusion patent technology supports two recognition modes of near infrared and visible light. The algorithm is in the forefront of the FRGC International face recognition challenge. These achievements cannot be separated from practice.
As a leading enterprise in the field of biological recognition for nearly 20 years, Tiancheng Shengye has accumulated a billion-level face recognition database due to abundant cases. Moreover, with the development of the company, the face recognition database has been constantly updated and expanded. This huge database full of fresh blood is especially important for the evolution of face recognition algorithms.
There is a saying that I also know you when it turns into ash. In fact, it means that you are familiar with a person. You can recognize what it becomes. This sentence is also used in face recognition algorithms, for the influence of external factors such as cosmetic surgery and age change, the face recognition rate will also increase with the increase of processed face data.
To make good use of face recognition algorithms requires platform strategies. In practical applications, complex and changeable application scenarios also make the application of face recognition algorithms face great challenges.
Based on the rich practical experience of the project, Tiancheng Shengye has proposed the platform strategy. Through the multi-modal biometric unified identity authentication platform, relying on the strong compatibility of the platform, the enterprise does not need to modify the existing business system, the face recognition algorithm can be flexibly introduced into multiple application scenarios to meet the needs of different application scenarios in the future. Moreover, the unified and centralized identity authentication management function and log management function are provided, it is conducive to building a big data model in all directions, so that face recognition algorithms can be better optimized through autonomous learning based on accumulated databases.
It can be said that using the platform strategy makes the application of face recognition algorithm more intelligent, convenient and fast!