Clustering and reclassifying face recognition method

Abstract

The invention discloses a clustering and reclassifying face recognition method, which comprises the steps of acquiring a training sample; carrying out equalization processing on the training sample; carrying out Gabor texture feature extraction on face images, and acquiring a feature vector corresponding to each face image after feature extraction; carrying out dimension reduction on acquired Gabor texture features of each face image to acquire feature vectors after dimension reduction; carrying out a clustering operation until distance convergence so as to complete clustering; classifying all of the clustered feature vectors to acquire a plurality of subclasses, calculating to determine each vector mean value, and calculating to acquire a within-class distance and an among-class distance; carrying out feature extraction and preprocessing on face images of a target to be recognized, acquiring a feature vector after projection transformation, and calculating the distance between the acquired feature vector and the feature vectors in each subclass sequentially so as to acquire the similarity; and determining identity information of the target to be recognized. The method disclosed by the invention can shorten the among-class distance so as to reduce an error in the acquisition process, and the accuracy of face recognition is improved.

Description

technical field [0001] The invention relates to a face recognition method based on clustering and reclassification, and belongs to the technical field of video image processing. Background technique [0002] Face recognition is a common technology in modern life. It is a biometric-based recognition method. Compared with fingerprint and iris recognition, which belong to biometric recognition, face recognition does not require direct contact and does not require special External devices have the advantage of being simple and fast. Therefore, face recognition technology is widely used in many fields, and face feature extraction and pattern recognition in face recognition technology are one of the hot spots based on biometric research in recent years. [0003] At present, face recognition technology has been widely used in government, banking, e-commerce, security and defense and other fields. For example, bank depositors can deposit and withdraw directly from ATMs equipped wi...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a face recognition method of clustering and reclassification, and solve the problem of face image samples in the face recognition method of the prior art, such as angles, expressions, etc. The problem of local information loss for calculating the mean value of face feature vectors caused by differences

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