Face identification method based on wavelet multi-scale analysis and local binary pattern

Abstract

The invention, which relates to the technical fields including pattern identification, image processing and computer vision and the like, provides a face identification method based on a wavelet multi-scale analysis and a local binary pattern (LTP). The method comprises the following steps: selecting an appropriate face image; carrying out a multi-scale wavelet analysis on a training image to obtain a first-level low frequency approximation image and a second-level low frequency approximation image; utilizing an LTP algorithm to carry out conversion on the low frequency approximation images to obtain LTP characteristic values of all pixel points; carrying out statistics on LTP histograms of the images by utilizing a blocking method and connecting the blocked histograms of the images of the two levels to obtain characteristic vector representation of the face image; and for a to-be-identified face, obtaining a characteristic vector of the to-be-identified face image and then using X <2> probability statistics to complete face identification. According to the method provided by the invention, an image noise effect can be effectively reduced; extraction capability for image texture characteristics can be enhanced; besides, the method has advantages of good robustness, high identification rate, fast calculating speed and important practical value.

Description

technical field [0001] The invention relates to the technical fields of pattern recognition, image processing and computer vision, in particular to a face recognition method based on wavelet multi-scale analysis and local ternary patterns. Background technique [0002] Biometric identification technology refers to the use of individual physiological characteristics (such as fingerprints, iris, face, palmprint, retina, etc.) or behavioral characteristics (such as writing, voice, keystrokes, etc.) to achieve the purpose of identification and verification. science. Compared with traditional identity verification methods, the outstanding advantages of biometrics are that biometrics can fundamentally eliminate forgery and theft, and are owned by human beings, so they have higher reliability, security and usability. Among the many biometrics, fingerprints, irises, voices, and faces are widely used. Among them, the human face, as one of the most important human biological charact...

AI Technical Summary

Problems solved by technology

Classical methods such as principal component analysis (PCA) obtain the global features of the image by finding the best projection direction for the training data set. Although this method describes the face well, it is easily affected by factors such as illumination and location, and Not applicable when the number of dimensions is too high

Wang Yan of Shanghai University et al. (CN 102024141A) proposed a method of fusing Gabor wavelet transform and local binary mode, which solved the problem of difficult feature extraction under high-dimensional data. However, convolution operations on images with 40 Gabor filters are extremely Increased the complexity of the method, the presence of image noise will also affect the performance of the method

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