Face recognition method based on dictionary learning models

Abstract

The invention discloses a face recognition method based on dictionary learning models. The method comprises the following steps of: mapping trained and tested face images to a low-dimension space to acquire a training signal set matrix; establishing the dictionary learning models which comprise an irrelevant dictionary learning (IDL) model and an unconstrained irrelevant dictionary learning (U-IDL) model; inputting the training signal set matrix into the IDL and U-IDL models, and solving the models to acquire an irrelevant dictionary and a linear classifier; acquiring a corresponding sparse vector of each picture belonging to a test sample based on the dictionary acquired in the last step by using a sparse expression algorithm; and inputting the sparse vectors into the linear classifier to acquire category labels of test sample pictures, wherein the result expressed by the category labels is used as the face recognition result. The invention provides the new models and the new method for dictionary learning problems in sparse expression, and the models and the method can be applied to mode identification and image classification problem under common conditions; and particularly, aiming at face recognition application, the dictionary learning method can achieve relatively high face recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a face recognition method based on a dictionary learning model. Background technique [0002] Sparse representation technology based on overcomplete dictionary is a hot topic in the fields of computer vision, pattern recognition, and machine learning. It has been used in image denoising and patching, face recognition, image classification, video abnormal behavior detection, etc There have been many successful applications in the research field. [0003] Given a column vector consisting of K base signals An overcomplete dictionary composed of columns is a set of real numbers, n is the dimension of the base signal column vector, and K is the number of base signal column vectors), for the input signal column vector in n-dimensional space The sparse representation problem can be expressed as: [0004] min x ...

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Problems solved by technology

[0008] If the base signal d in the dictionary D k If the dimension of the linearly stretched subspace is too different from the dimension of the space where the input signal y is located, using such a dictionary, even if it has a good reconstruction ability for the training data, it is difficult for the new test data. good generalization performance

The research results show that the degree of uncorrelation of the base signal in the dictionary is of great help to improve the accuracy of sparse representation reconstruction and the algorithm operation speed, but most of the sparse representation and dictionary learning models at this stage only focus on the reconstruction of the dictionary as a whole Performance and discriminative performance, ignoring the important factor of the uncorrelated degree of the base signal

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