A texture feature extraction and recognition method for convex-concave patterns in human face images

Abstract

The invention relates to a method for extracting and recognizing texture features of a convex-concave pattern of a face image, and belongs to the technical field of pattern recognition. The invention first divides the image into blocks, then performs bilinear interpolation on each divided image, then calculates the local difference of each pixel point in the divided image along 8 directions, and encodes the convex and concave characteristics of the local difference to obtain the image The multi-resolution local convex-concave characteristic matrix of the block, then extract the histogram feature vector of the multi-resolution local convex-concave characteristic matrix of this image block, connect the histogram feature vectors of each block image in turn to obtain the histogram feature vector of the original image, and finally The feature vector is sent to the nearest neighbor classifier based on chi-square statistics for classification and identification. The invention encodes the local convex and concave characteristic of the local difference of the image, and the local convex and concave characteristic indicates a characteristic of the local gray scale fluctuation of the image, has a strong ability to describe the local image texture, and can effectively perform face recognition under the illumination environment.

Description

technical field [0001] The invention relates to a method for extracting and identifying texture features of a convex-convex pattern of a human face image, and belongs to the technical field of pattern recognition. Background technique [0002] Local binary pattern (LBP) [L.Wang and D.C.He, "Texture classification using texture spectrum", Pattern Recognition, vol.23, pp.905-910, 1990.] is an important image feature extraction The operator has the characteristics of small amount of calculation and effective. Although LBP has achieved great success in the fields of computer vision and pattern recognition, its working mechanism still needs to be improved. Dominant local binary patterns (DLBP) [S.Liao, M.W.K.Law, and A.C.S.Chung, "Dominant local binary patterns for texture classification," IEEE Trans.ImageProcess., vol.18, no.5, pp.1107–1118, May 2009.] On the basis of all the LBP modes of the statistical image, the higher frequency modes are screened out, and the high frequenc...

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Problems solved by technology

[0004] The present invention provides a method for extracting and identifying texture features of convex-convex patterns of human face images, which is used to solve the problem of human face recognition under illumination environment

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