Multi-class Bagging gait recognition method based on multi-characteristic attribute

Abstract

The invention relates to a multi-class Bagging gait recognition method based on a multi-gait characteristic attribute, which belongs to the technical field of pattern recognition. According to the method, a nearest neighbor classifier is used as a weak classifier, and an integration classifier is constructed by expanding a two-class attribute Bagging method to a plurality of classes on the basis of 20 gait attribute characteristic sets after wavelet packets are decomposed and principal components are completely analyzed so as to carry out gait identity identification. The method comprises the following steps of: preprocessing, extracting characteristics and finally classifying test samples by using a method combining a nearest neighbor classifying principle and an MCAB algorithm. According to the multi-class Bagging gait recognition method based on the multi-gait characteristic attribute, a method fusing wavelet packet decomposition (WPD) and (2D) 2 principal component analysis (PCA) is adopted for the first time to extract and also select gait characteristics. The problem of loss of high-frequency components in a traditional gait recognition method based on wavelet transformation or overlarge dimensionality caused by simply adopting all data is solved. The multi-class Bagging gait recognition method based on the multi-gait characteristic attribute has higher recognition rate and visual angle change robustness.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a new multi-category bagging gait recognition method based on multi-gait characteristic attributes, which is a method of realizing human gait by using computer technology, digital image processing technology, pattern recognition, etc. The method of automatic analysis and discrimination is an algorithm for gait feature extraction and recognition in the field of biometric identification. Background technique [0002] Biometric identification technology refers to the technology that uses the physiological characteristics or behavioral characteristics that human beings possess to identify their identity for identity verification. Compared with traditional identity verification technology, biometric technology fundamentally eliminates forgery and theft, has higher reliability and security, and has been more and more widely used in identity authentication of som...

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Problems solved by technology

[0004] Regarding the feature extraction technology of gait, some literatures use wavelet packet decomposition to solve this problem better, but the image feature dimension after wavelet packet decomposition is relatively high, and it uses the classic PCA algorithm for feature extraction, that is, using singular When the value decomposition method is used to find the eigenvalues ​​and eigenvectors of the correlation matrix, the calculation is expensive

Two-dimensional principal component analysis (2DPCA) can directly calculate the image data matrix, and the calculation amount is relatively small, but n*k is required after 2DPCA (where n is the image resolution, k is the number of feature column vectors selected after transformation, And k<n) data to represent the image, the dimension of the feature vector is still high

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