Cross domain meta-network for sketch face recognition

Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first des...

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Bibliographic Details
Published inMATEC Web of Conferences Vol. 336; p. 6007
Main Authors Shao, Yuying, Cao, Lin, Chen, Changwu, Du, Kangning
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 2021
Subjects
Deep learning
Domains
Face recognition
Machine learning
Object recognition
Training
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Summary:Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first designs a meta-learning training strategy to solve the small sample problem, and then proposes entropy average loss and cross domain adaptive loss to reduce the modal difference between the sketch domain and the optical domain. The experimental results on UoM-SGFS and PRIP-VSGC sketch face data sets show that this method and other sketch face recognition methods.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202133606007