Cross-age face recognition method based on attention mechanism and data imbalance

A technology of attention and age people, applied in the field of cross-age face recognition solutions, can solve problems such as uneven distribution of age labels, loss of identity information, and data distribution affecting the effect of cross-age face recognition, so as to alleviate the distribution of age data Effects of Imbalanced, Robust Identity Features

Pending Publication Date: 2021-07-23
HANGZHOU DIANZI UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

Many discriminative methods simply extract identity features by removing the age factor in face features, but this method will lose identity information to a certain extent
In addition, deep learning has a huge dependence on data sets, and cross-age face data se

Method used

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  • Cross-age face recognition method based on attention mechanism and data imbalance
  • Cross-age face recognition method based on attention mechanism and data imbalance
  • Cross-age face recognition method based on attention mechanism and data imbalance

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Embodiment Construction

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and through specific embodiments.

[0042] The network structure used in this method is as follows figure 2 As shown, firstly, a feature extraction network is used to extract the input features, and then the self-attention module processes the extracted facial feature sequence, and the feature fusion part fuses the facial feature sequence, and then the age feature and age feature are obtained through the feature decomposition module. Identity features, the corresponding age and identity are estimated by the classifier.

[0043] Such as figure 1 Shown, specific embodiment of the present invention and its implementation process are as follows:

[0044] The specific implementation process includes two stages of training and testing of the deep model:

[0045] Step 1: Select three face photo samples from the Morph dataset, with identity p: Take the age corresp...

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Abstract

The invention discloses a cross-age face recognition method based on an attention mechanism and data imbalance. The method comprises the following steps: firstly, extracting facial photos of different ages of the same person from a cross-age face recognition data set to form a face time sequence, and processing through a self-attention mechanism to obtain a processed feature sequence; performing linear fusion on the feature sequence processed by the self-attention mechanism; decomposing the fused facial features into identity features and age features; and supervising the age estimation task and the identity estimation task by adopting the reweighted age loss and identity loss, and constraining the similarity between the age feature and the identity feature by adopting the correlation loss. According to the method, facial photos of different ages of the same identity and a self-attention mechanism are fused, the influence of the ages on the face is learned, and more robust identity features are extracted; by re-weighting the age loss, the accuracy reduction caused by age data distribution imbalance is relieved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a cross-age face recognition scheme based on an attention mechanism and data balance, a cross-age face recognition scheme. Background technique [0002] With the development of social economy and science and technology, face recognition has been widely used in various industries and has achieved impressive performance. However, in the face of face aging with age, general face recognition schemes have exposed their defects of low robustness, and cannot accurately and stably recognize faces with large age differences. [0003] As people age, sometimes the gaps in faces between different ages are even greater than the gaps between different people. Therefore, a key challenge of cross-age face recognition is to extract age-independent identity features from faces, so as to overcome the influence of age. In general, cross-age face recognition can be divided into generative methods and...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N20/00
CPCG06N20/00G06V40/161G06V40/168G06F18/22G06F18/253G06F18/214
Inventor 颜成钢张杰华孙垚棋张继勇李宗鹏张勇东
Owner HANGZHOU DIANZI UNIV
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