A Face Recognition Method Based on Low Rank Dictionary Learning Algorithm

Abstract

The present invention relates to a kind of face recognition method based on low-rank dictionary learning algorithm, it is characterized in that comprising the following steps: one, input training face image and utilize the Fisher discriminant constraint item of K-SVD algorithm construction profiles; The classification model of the face image test sample to test the class mark of the face image; third, collect and input the data of the face image to be recognized to the classification model; finally the classification model outputs the class mark of the test face image, and then face recognition. This method not only improves the discriminative performance of face recognition, but also reduces the complexity of the algorithm, thereby improving the effect of the face recognition system.

Description

technical field [0001] The invention relates to a face recognition method based on a low-rank dictionary learning algorithm. Background technique [0002] Profiles are row vectors of the encoding coefficient matrix, which have a one-to-one correspondence with atoms, and can reflect the situation that the corresponding atoms are used in the dictionary learning process. At present, dictionary learning algorithms often use coding coefficients and training sample features to construct constraints, but face images are easily affected by factors such as illumination, posture, and occlusion, which makes coding coefficients vulnerable to pollution, which affects the direct use of face images and coding coefficients to construct constraints. Robustness of terms, degrades the classification performance of dictionary learning-based face recognition systems. SUMMARY OF THE INVENTION [0003] Aiming at the shortcomings of the prior art, the present invention provides a face recognitio...

AI Technical Summary

Problems solved by technology

At present, dictionary learning algorithms often use encoding coefficients and training sample features to construct constraint items, but face images are easily affected by factors such as illumination, posture, and occlusion, making encoding coefficients susceptible to contamination, affecting the direct use of face images and encoding coefficients to construct constraints. Robustness of the term, reducing the classification performance of a dictionary learning-based face recognition system

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