A three-dimensional face recognition method based on sparse features

Abstract

The invention provides a three-dimensional face recognition method based on sparse features. The method comprises the steps of: S1, acquiring three-dimensional face information and pre-processing thethree-dimensional face information; S2, extracting sparse features with classification information through sparse representation and dictionary learning of the three-dimensional face information; S3,performing class prediction on the extracted sparse features by using a classifier to obtain a final classification result. The three-dimensional face recognition method based on sparse features has the advantages that the K-SVD algorithm integrating sparse representation and dictionary learning is used for feature extraction of three-dimensional face models, so that the data dimensionality and the calculation cost are reduced; the algorithm is simple and easy to implement and has higher recognition rate and greater robustness.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a three-dimensional face recognition method based on sparse features. Background technique [0002] In recent years, with the development of 3D data acquisition technology, 3D face recognition has become a research hotspot in computer graphics and computer vision. Compared with two-dimensional face image recognition, three-dimensional face recognition incorporates geometric information such as surface shape, and is not easily affected by changes in illumination, expression, posture, etc., improves the accuracy of recognition, and has a broader market prospect. [0003] In 3D face recognition, the face information is relatively complicated. How to extract the low-dimensional and effective feature information of 3D face is the key to improving the recognition accuracy. Scholars at home and abroad have done a lot of important work in this area, and have proposed Face feature extract...

AI Technical Summary

Problems solved by technology

[0003] In 3D face recognition, the face information is relatively complicated. How to extract the low-dimensional and effective feature information of 3D face is the key to improving the recognition accuracy. Scholars at home and abroad have done a lot of important work in this area, and have proposed Face feature extraction methods such as PCA and geodesic, but there are shortcomings such as large time overhead

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