Domain Alignment Embedding Network for Sketch Face Recognition

• Published in: IEEE access Vol. 9; pp. 872 - 882
• Main Authors: Guo, Yanan, Cao, Lin, Chen, Changwu, Du, Kangning, Fu, Chong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Deep learning; deep metric learning; Domains; Embedding; Face recognition; feature embedding network; Feature extraction; Feature recognition; Information science; Learning systems; Machine learning; Measurement; Sketch face recognition; Sketches; small sample problem; Task analysis; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3047108

Sketch face recognition refers to the process of matching sketches to photos. Recently, there has been a growing interest in using deep learning to learn discriminative features for sketch face recognition. However, the success of deep learning relies on the large-scale paired images to counteract effects such as over-fitting, since the amount of the paired training data is relatively small, the discriminative power of the deeply learned features will inevitably be reduced. This paper proposes a novel deep metric learning method termed domain alignment embedding network for sketch face recognition. Specifically, a training episode strategy is designed to alleviate the small sample problem, and a domain alignment embedding loss is proposed to guide the feature embedding network to learn discriminative features. Extensive experimental results on the UoM-SGFSv2 and PRIP-VSGC datasets are verified to show the effectiveness of the proposed method.