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Cost-sensitive incremental face recognition method based on information entropy selection

A cost-sensitive face recognition technology, applied in the field of face recognition, can solve the problems of unbalanced misclassification cost and high training cost, so as to avoid high cost misclassification, improve recognition accuracy, and reduce misclassification cost Effect

Pending Publication Date: 2019-06-25
NANJING UNIV
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AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the traditional face recognition system has high training cost and does not consider the problem of misclassification cost imbalance

Method used

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  • Cost-sensitive incremental face recognition method based on information entropy selection
  • Cost-sensitive incremental face recognition method based on information entropy selection
  • Cost-sensitive incremental face recognition method based on information entropy selection

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Embodiment Construction

[0038] like figure 1 As shown, the cost-sensitive incremental face recognition method based on information entropy selection disclosed by the invention includes the following steps:

[0039] Step 1, input the unlabeled sample set U and the test sample set V, divide the unlabeled sample set U and the test sample set V into the positive sample set S according to the ratio of 3:1 P and the negative sample set S N , and set the cost loss function λ PN , lambda NP , lambda BN , lambda BP , lambda NN and lambda PP ;

[0040] Step 2, extract 10% of the unlabeled samples from the unlabeled sample set U for labeling to form the labeled sample set L;

[0041] Step 3, using the labeled sample set L to train the deep convolutional neural network model M;

[0042] Step 4, use the trained deep convolutional neural network model M to each unlabeled sample u in the unlabeled sample set U i Perform Softmax classifier classification to get each unlabeled sample u i The Softmax probab...

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Abstract

The invention provides a cost-sensitive incremental face recognition method based on information entropy selection. The cost-sensitive incremental face recognition method is composed of a deep convolutional neural network part, an information entropy-based sample selection part and a cost-sensitive sequential three-branch decision classification part. The information entropy is used for assessingthe information amount of the classification result of the face recognition sample, so that the system can automatically assess the unlabeled sample information amount, and samples with large information amount are selected for manual labeling; a face recognition problem is regarded as a sequential process of information granularity from coarse to fine by utilizing the thought of three sequentialdecisions sensitive to cost, each iterative loop added with a marked sample is used as a decision step of the three sequential decisions, and the minimum cost recognition effect of the sample in eachdecision step is given according to the minimum Bayesian risk principle.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a cost-sensitive incremental face recognition method based on information entropy selection. Background technique [0002] Face recognition technology is an important technology in the field of image information processing and artificial intelligence. This technology is mainly based on human facial features for identity authentication. Compared with other biometric technologies, face recognition has the advantage of being non-invasive. It only requires the user to be within the field of view of the camera for identification. It is widely used in the military, border defense, justice, finance, factories, education, and medical care. and other fields. [0003] With the development of science and technology, the accuracy of face recognition has reached a very high level, but training a classifier with better performance (such as deep convolutional neural network) requires a large number...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCY02T10/40
Inventor 李华雄顾心诚辛博
Owner NANJING UNIV
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