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Sparse representation based incremental face recognition method

A technology of face recognition and sparse representation, which is applied in character and pattern recognition, instruments, computer components, etc., and can solve the problems of high time cost

Active Publication Date: 2015-10-14
NANJING UNIV
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Problems solved by technology

Like the commonly used SVM, the training model of the neural network needs to be retrained to find a new model for the newly added samples, which takes a lot of time.

Method used

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  • Sparse representation based incremental face recognition method
  • Sparse representation based incremental face recognition method
  • Sparse representation based incremental face recognition method

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Embodiment Construction

[0056] The incremental face recognition method based on sparse representation is divided into 3 parts, such as Figure 5 Shown is the flow chart of the present invention, the training part, the testing part and the incremental part. The training part is responsible for the generation of the component dictionary in the training set; the test part is responsible for the dynamic generation of the global face in each subset and the generation of the final global result when the picture to be detected is input; the incremental part is responsible for the component dictionary generated in the training set when new samples are added update.

[0057] like figure 1 As shown, the left picture is the result of face detection and key point positioning, and the right picture is the extracted four face parts. In the left picture, a total of 7 key points are obtained, which are the two corners of the left and right eyes, the center of the bridge of the nose, and the two corners of the mout...

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Abstract

The invention provides a sparse representation based incremental face recognition method which comprises the steps of: obtaining face key point information through face detection, determining face local block positions, extracting HOG features of each local block, constructing a local face dictionary for each local block according to a sparse representation classification method, and dynamically selecting part features according to a test picture during testing to generate a global face dictionary. For the face test picture, local block features are extracted, a test is carried out on the corresponding local face dictionary, the quality of face parts is judged according to an obtained local result, qualified parts are selected out according to a quality result, the features of the qualified parts in a training set are connected to construct the global face dictionary, global face features of the test picture are constructed, and an obtained global result is a final result. The whole training set is divided into N mutually exclusive subsets by type in incremental processing, processing is carried out in each subset to obtain a judgment result, and competition is carried out in all the subsets to obtain a final result.

Description

technical field [0001] The invention relates to the field of computer image processing, in particular to an incremental face recognition method based on sparse representation. Background technique [0002] Face recognition technology has been widely used in various authentication systems such as access control and punch card systems. However, there are still many problems to be considered in face recognition in practice, such as changes in facial expression deflection, glasses occlusion, etc., which will bring about great changes in the face and make recognition difficult. In addition, a practical face recognition system also needs to consider the processing of newly added samples, that is, the incremental problem of face recognition, how to quickly and effectively update the training model and ensure that the recognition rate is not affected. Like the commonly used SVM, the training model of the neural network needs to be retrained to find a new model for the newly added s...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/172
Inventor 杨若瑜叶君健
Owner NANJING UNIV
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