Cooperative sparse representation self-adaptive rapid face recognition method

Abstract

The invention relates to a cooperative sparse representation self-adaptive rapid face recognition method. The method includes a local sparse representation classifier system that does not violate a sparse representation definition fundamental assumption, and includes the steps of: reading in images of training samples and a test sample; initializing the training samples and the test sample, using bilinearity interpolation to scale the training samples and the test sample to images of fixed sizes, integrating into column vectors and performing normalization processing; using nucleus induction to find out N* training samples most adjacent to the test sample, N* being an optimal predicted value; picking out a training sample category related with the test sample from the N* training samples to form a complete base; and using I<2> norm collaboration to solve a sparse coefficient and predicting the category of the test sample through a residual error. The method also includes a system capable of finding the optimal predicted value N* according to different training sample libraries. The rapid face recognition method provided by the invention solves the problem of balancing the recognition rate and the calculation speed, and enables a whole recognition system to automatically search an appropriate N value for different training libraries.

Description

technical field [0001] The invention relates to a cooperative sparse representation self-adaptive fast face recognition method. Background technique [0002] Face recognition technology has always been an important research hotspot in the field of pattern recognition and computer vision. The so-called face recognition refers to predicting the label class that the test sample should belong to from the already-labeled face training samples. As we all know, external factors (such as whether the face wears glasses, the intensity of light, the angle of the face, etc.) will have a great impact on face recognition, and even for different training libraries, the face recognition rate varies. Different effects, it is challenging to get an adaptive face recognition system. [0003] Sparse representation based classifier is the key technology of face recognition. This technology directly trains all training sample sets to obtain a complete base, and uses this complete base to identi...

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Problems solved by technology

[0007] In view of this, the purpose of the present invention is to provide a cooperative sparse representation adaptive fast face recognition method, which solves the problem of balancing the recognition rate and calculation rate, and at the same time enables the entire recognition system to automatically find a suitable face recognition method for different training libraries. N value

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