The classification of recognition algorithms
Generally, face recognition includes image grabbing, face location, and image preprocessing. The system input is generally a series of face images containing unidentified identities, as well as face images of certain known identities in the face database or corresponding codes, and the output is a series of similarity scores indicating the identity of the face to be identified
Feature-based recognition algorithms, appearance-based recognition algorithms, template-based recognition algorithms and recognition algorithms using neural network.
Face recognition needs collect a huge amount of related data about face images, using for verification algorithms, improve the accuracy of recognition constantly, these data such as A Neural Network Face Recognition Assignment, Orl face database, the face recognition database of MIT biological and computational learning center and others.
The advantage of face recognition lies in its naturalness and characteristics that are not perceived by the individual being tested.
The so-called naturalness means that the recognition method is the same as that used by humans for individual identification. For example face recognition, humans also distinguish and recognize the identity by observing the face, and also have natural recognition, speech recognition, body shape recognition, etc., while fingerprint recognition and iris recognition are not natural, because human or other Creatures do not distinguish individuals by such biological characteristics.
Undetected features are also important for an identification method, which makes the recognition method unobjectionable and not easily fooled because it is not easily noticeable. Face recognition has this feature, it completely uses visible light to obtain facial image information. Unlike fingerprint recognition or iris recognition, it is necessary to use electronic pressure sensors to collect fingerprints, or use infrared to collect iris images. These special collection methods are easy. Being perceived, it is more likely to be deceived by pretense.