Classification and aggregation sparse representation face identification method based on nuclear space

Abstract

The present invention relates to a classification and aggregation sparse representation face identification method based on a nuclear space. The method comprises the following steps: employing a convolutional neural network to extract facial features of a facial image, training a classification and aggregation dictionary, and identifying the image. The classification and aggregation sparse representation face identification method based on the nuclear space considers that the weighting of each training sample to the subspace construction is different and the train samples closed to a class center should have bigger weight to the subspace construction when the train samples are configured to perform sparse representation of test samples, a [Phi](Xc)Wc matrix is adopted to construct a new sparse representation dictionary, and classification concentration constraint terms are added in a sparse representation constraint. Compared with the prior, the classification and aggregation sparse representation face identification method based on the nuclear space is able to effectively reduce the fitting error of test samples in corresponding subspaces to allow samples with the same type to aggregate in the sparse representation so as to improve the face identification performance, enhance the capabilities of processing the non-linear structure and relation, effectively excavate hiding features of complex data and further improve the face identification performance.

Description

technical field [0001] The technical field of pattern recognition of the present invention, in particular, relates to a face recognition method based on classification aggregation sparse representation of kernel space. Background technique [0002] With the development of science and technology, many fields are faced with an increasingly large amount of data, such as seismic data, geophysical data, audio data, astronomical data, industrial control data, genetic data, etc. How to realize flexible and effective processing of these huge data And adaptive expression has gradually become one of the concerns of people. Image processing, information transmission, computer vision and many other fields have been seeking sparse and concise representations of signals and images. The advantage of this sparse representation is that non-zero coefficients reveal the internal structure and essential properties of signals and images. Nonzero coefficients have explicit physical meaning. [...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the above-mentioned shortcomings of the existing face recognition methods, such as large fitting error and low accuracy, the present invention provides a face recognition method based on classification and aggregation sparse representation of kernel space, which heats classification concentration constraints in the sparse expression constraints , so that the samples of the same type are more aggregated in the subspace, and samples of different types are relatively dispersed in the subspace, which is conducive to the clustering of samples in the high-dimensional subspace and improves the recognition effect

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