Multidirectional SLGS characteristic description and performance cloud weight fusion face recognition method

Abstract

The invention discloses a multidirectional SLGS characteristic description and performance cloud weight fusion face recognition method. The face recognition method comprises the following steps: 1, an existing SLGS algorithm is extended from a perspective of directions, and textural features of a human face in different directions are obtained; 2, base classifiers are constructed based on the texture features by utilizing a layered cross-processing mode, a performance cloud is formed according to recognition stability and reliability of the base classifiers on different regions, and weight values are obtained; and 3, discrimination and classification for a to-be-measured human face are achieved through a weight fusion for the base classifiers. The method can use a multidirectional SLGS algorithm to fully describe a human face image, and can use the weight values, obtained based on the performance cloud, of the base classifiers to improve recognition performance of a system and obtain the high recognition rate.

Description

technical field [0001] The invention relates to a feature extraction method and integrated discrimination, and belongs to the field of pattern recognition, in particular to a multi-directional SLGS feature description and performance cloud weighted fusion face recognition method. Background technique [0002] Face recognition is a research hotspot in the field of image processing and computer vision in recent years. It has greatly promoted many related disciplines and has received extensive attention from researchers. Face recognition problems mainly develop along two main lines: feature description and object matching of face images. Feature description is the core step of face recognition. The ideal feature description should only reflect the changes in the essential attributes of the face due to the difference in appearance, and is insensitive to changes in expressions and lighting. Widely known feature extraction algorithms include PCA algorithm, Gabor algorithm, sparse...

AI Technical Summary

Problems solved by technology

But they generally have a defect: an important indicator for judging the performance of classifiers is that the model is statistically optimal for the training sample set, without considering the recognition stability and reliability of the base classifier in different regions of the sample space , that is, the specific situation of each sample, lacking a description of the reliability of a certain sample

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