A Sparse Representation Classification Method Based on Common Vector Dictionary

Abstract

The invention proposes a common vector based sparse representation classification method. The method comprises the steps of firstly, performing Gram-Schmidt orthogonal transformation through a differential sub-space of each training sample type to obtain a common vector of each type; secondly, by taking a dictionary composed of all common vectors as a dictionary of a sparse representation classifier, calculating a sparse coefficient of a test sample in the dictionary through l1-norm minimization; and obtaining an estimated test sample by using the sparse coefficient corresponding to each type and the training sample of the type, comparing the estimated test sample with the acquired test sample, and taking the type with the highest similarity as a classification result. The core thought of the invention is that a dictionary, consisting of all training samples, of an original sparse representation classifier is replaced with the dictionary consisting of the common vectors of all types in the training samples, thereby remarkably increasing the correctness rate of a human face identification method in a small sample condition.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a sparse representation classification method based on a common vector dictionary. Background technique [0002] Face recognition is one of the most challenging research directions in the field of pattern recognition, machine learning and computer vision. Face recognition research covers a wide range, including pattern recognition, image processing, artificial intelligence, etc. Identification methods usually include fingerprints, palm prints, infrared thermometers, voiceprints, and faces. In contrast, face recognition has a more convenient collection method and faster operation. Among the various forms of biological characteristics of people, the human face is the most natural and main feature that distinguishes a person from others. Face features are unique, and the faces of one person are different from others, even the faces of twins are different, which shows that it is re...

AI Technical Summary

Problems solved by technology

[0006] Generally speaking, classifiers based on sparse representation need to form a complete dictionary through a large number of training samples for each individual to be identified in order to construct sparse conditions, and the recognition effect for small sample situations is not ideal.

However, the small sample problem is a common phenomenon in face recognition

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