Human face recognition method based on supervision isometric projection

Abstract

The invention provides a human face recognition method based on supervision isometric projection. The human face recognition method comprises the human face sample training process and the human facesample testing process. The human face sample training process comprises the following steps: firstly carrying out pretreatment on a human face training image, adopting Gabor wavelet for filtering theimage, proposing a new distance formula for calculating an adjacency matrix of a training sample, calculating a shortest path distance matrix D in the training sample by the adjacency matrix DG of the training sample, calculating a low-dimensional projection matrix describing data of the human face training sample, calculating the projection of the training sample in low-dimensional space througha projection conversion matrix A and the like; and the human face sample testing process further comprises the following steps; carrying out the pretreatment on a human face testing image, adopting the Gabor wavelet for filtering the image, calculating the projection of the testing image in the low-dimensional space, adopting a nearest neighbor algorithm for judging the type of a testing sample and the like. The human face recognition method is characterized by stronger description of the structure of the sample data, elimination of high-order redundancy and small calculation cost, thereby being more applicable to mode classification tasks and the like.

Description

(1) Technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a face recognition method based on supervised isometric projection. (2) Background technology [0002] In recent years, face recognition has received extensive attention in the field of pattern recognition. Subspace analysis is an important method in the field of face recognition. Subspace analysis has the characteristics of strong descriptiveness, low computational cost, easy implementation and good separability. , so it has become a research hotspot in the field of face recognition, and the two most widely used algorithms are PCA (Principal Components Analysis, PCA) and LDA (Linear Discriminant Analysis, LDA). PCA is an unsupervised learning method whose goal is to find the subspace that gives the optimal representation of the data in the least squares sense. LDA is a supervised learning method that finds the optimal linear discriminant space by maximi...

AI Technical Summary

Problems solved by technology

However, a major disadvantage of the current nonlinear manifold learning algorithm is that it is difficult to process new data, that is, it is only processed on the training data, and the computational complexity of the test data points is relatively high for recognition.

This defect leads to great limitations in the practical application of nonlinear algorithms

In order to solve this problem, some scholars have proposed the linearization algorithm of the above-mentioned nonlinear manifold learning algorithm, such as Locality Preserving Projection (Locality Preserving Projection, LPP) and Neighborhood Preserving Embedding (NPE), as a linear algorithm, These algorithms can directly obtain the low-dimensional mapping results of new data points, and at the same time can effectively describe the manifold structure of the data, and have strong practicability in the field of pattern recognition such as face recognition. Algorithms, when doing pattern classification, have certain limitations

In addition, the manifold learning algorithm cannot effectively eliminate redundant information such as high-order correlation in the image, which affects the recognition rate of the algorithm

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