Face recognition method integrating EM algorithm and probabilistic two-dimensional CCA

Abstract

The invention discloses a face recognition method integrating EM algorithm and probabilistic two-dimensional CCA. Firstly, a probabilistic two-dimensional CCA model is established, and logarithmic likelihood expectation of left probabilistic model and right probabilistic model is calculated respectively. Then EM algorithm is used to maximize the expectation of logarithm likelihood to estimate theparameters of the model and optimize the left-right probability model. Finally, the probabilistic two-dimensional CCA projection matrix is used to project the observed data into the hidden space to reduce the dimension of the high-dimensional data, which greatly reduces the computational complexity and improves the accuracy of face recognition. A large number of test and recognition of the AR facedatabase verifies the robustness and superiority of the method proposed by the invention in dealing with the face samples of the illumination change, the expression change and the attitude change, and solves the dimension disaster and the small sample problem of the existing face recognition method in dealing with the high-dimensional data.

Description

Technical field: [0001] The invention relates to a face recognition method, in particular to a face recognition method combining EM algorithm and probability two-dimensional CCA. Background technique: [0002] Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied to image feature extraction. Instead of concatenating the columns of an image to a 1D vector, the method works directly with a 2D image matrix. Although 2DCCA works well in different recognition tasks, it lacks probabilistic interpretation. [0003] Many practical applications can be handled in high-dimensional data, but the most informative parts of the data can be modeled in low-dimensional spaces. Moreover, processing high-dimensional data is a time-consuming process that requires a lot of resources. To address these issues, feature extraction has been used as a tool to find compact and useful data representations. [0004] For unimodal source data, some subspace learn...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a combination of EM algorithm and probabilistic two-dimensional CCA, and solve the dimension disaster and small sample problem when the existing face recognition method deals with high-dimensional data. A Face Recognition Method Combining EM Algorithm and Probabilistic Two-Dimensional CCA

Images