Facial pose recognition method based on Gabor features and dictionary learning

Abstract

The invention discloses a facial pose recognition method based on Gabor features and dictionary learning. The facial pose recognition method comprises the following steps that firstly a facial pose is discretized into different subspaces, and a child dictionary is trained for each subspace by using K-SVD so that each subspace is enabled to be corresponding to one category; then all the dictionaries are combined into a super complete dictionary; and finally pose classification is performed by adopting a method based on the gabor features and sparse expression. A shielding face dictionary is reconstructed in order to enhance robustness of the algorithm so that the problem of face shielding in facial pose recognition can be solved. The problems of illumination, noise and shielding in facial pose estimation can be solved so that the front face, head raising, nodding, left deflection, the left side face, right deflection and the right side face can be rapidly and robustly recognized. The facial pose recognition method can be greatly applied to the field of safe driving, human-computer and face recognition.

Description

technical field [0001] The invention belongs to the technical fields of image processing, pattern recognition, computer vision and human-computer interaction, and relates to a face gesture recognition method, in particular to a face gesture recognition method based on dictionary learning and sparse representation. Background technique [0002] Facial pose estimation has great application prospects in the fields of intelligent video surveillance, face recognition, human-computer interaction and virtual reality. For example, in terms of intelligent video surveillance, face pose estimation can be applied to driving monitoring systems to identify whether the driver is concentrating on driving by monitoring changes in the driver's face pose to avoid collisions. In addition, face pose estimation has a great impact on the accuracy of face recognition. Many face recognition algorithms can achieve a good recognition rate for frontal face images, but for multi-pose non-frontal face im...

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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the above methods, propose a face gesture recognition method based on Gabor features and dictionary learning, solve the problems of illumination, noise and occlusion in the face gesture recognition, and identify the front, Head up, nod, left turn, left face, right turn and right face

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