What is the development history of face recognition?

The research of face recognition system began in 1960s, and was improved with the development of computer technology and optical imaging technology after 1980s, but it really entered the primary application stage in the late 1990s, which was mainly realized by technologies from the United States, Germany and Japan. The key to the success of face recognition system lies in whether it has the most advanced core algorithm and makes the recognition result have practical recognition rate and speed;

"Face recognition system" integrates artificial intelligence, machine recognition, machine learning, model theory, expert system, video image processing and other professional technologies. At the same time, it is necessary to combine the theory and implementation of median processing. This is the latest application of biometrics. The realization of its core technology shows the transformation from weak artificial intelligence to strong artificial intelligence.

Extended data:

Face image acquisition and detection

Face image acquisition: Different face images, such as static images, dynamic images, different postures and different expressions, can be acquired through the camera lens. When the user is within the shooting range of the acquisition device, the acquisition device will automatically search and shoot the user's face image.

Face detection: In practice, face detection is mainly used for preprocessing of face recognition, that is, accurately calibrating the position and size of the face in the image. Face images contain rich pattern features, such as histogram features, color features, template features, structural features and Haar features. Face detection is to pick out useful information and use these features to realize face detection.

The mainstream face detection method is based on the above characteristics and adopts Adaboost learning algorithm. Adaboost algorithm is a classification method, which combines some weak classification methods to form a new strong classification method.

In the process of face detection, Adaboost algorithm is used to select some rectangular features (weak classifiers) that best represent faces, and the weak classifiers are constructed into strong classifiers according to the weighted voting method, and then several trained strong classifiers are connected in series to form a cascade classifier, which effectively improves the detection speed of classifiers.

Baidu Encyclopedia-Face Recognition