Face identification method based on fuzzy rule

Abstract

The invention discloses a face identification method based on a fuzzy rule. The face identification method is a novel feature extraction method and belongs to the field of face identification. The method comprises the steps that a face image is divided; sub-image variance and information entropy are calculated as the output of a fuzzy controller, so as to acquire the fuzzy weight of a corresponding sub-image; and LBP extraction is carried out on the sub-image to acquire a histogram vector and PCA dimensionality reduction is carried out on a sub-image vector after LBP extraction. The fuzzy weight has a significant feature for the image identification rate. Through PCA dimensionality reduction, the computation time of an algorithm is saved, and the timeliness of the algorithm is improved.

Description

technical field [0001] The invention belongs to the field of face recognition, and in particular relates to a face recognition method based on fuzzy rules. Background technique [0002] Face recognition, specifically refers to the computer technology that uses the analysis and comparison of facial visual feature information for identity identification. It involves pattern recognition, image processing, computer vision, physiology, psychology and many other disciplines, and is a current research hotspot. Face recognition in a broad sense actually includes a series of related technologies for building a face recognition system, including face image acquisition, face positioning, face recognition preprocessing, identity confirmation, and identity search; A technology or system for face recognition or identity search. The system input is generally one or a series of face images with undetermined identities, as well as several face images with known identities in the face datab...

AI Technical Summary

Problems solved by technology

[0009] Although LBP has the above advantages, from the perspective of classification and recognition, it has the following shortcomings: Since the face image needs to be divided when extracting the LBP histogram, there will be a problem in the division process, that is, the division scale problem

Too many blocks will lead to too large dimension of the histogram vector, which will increase the computational complexity, and if there are too few blocks, it will lose statistical significance

The problem derived from the division problem is that LBP treats the division area equally, but this is not in line with common sense. It is generally believed that salient areas such as eyes, nose, and mouth have greater contributions to recognition, so these areas should be strengthened.

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