MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||5|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Cross domain meta-network for sketch face recognition
School of Information and Communication Engineering, Beijing Information Science and Technology University, China
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2021
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