SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person

Autor:	Hong, Sungeun, Im, Woobin, Ryu, Jongbin, Yang, Hyun S.
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Real-world face recognition using a single sample per person (SSPP) is a challenging task. The problem is exacerbated if the conditions under which the gallery image and the probe set are captured are completely different. To address these issues from the perspective of domain adaptation, we introduce an SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training. However, the SSPP characteristic of one training sample per class is insufficient to train the deep architecture. To overcome this shortage, we generate synthetic images with varying poses using a 3D face model. Experimental evaluations using a realistic SSPP dataset show that deep domain adaptation and image synthesis complement each other and dramatically improve accuracy. Experiments on a benchmark dataset using the proposed approach show state-of-the-art performance. All the dataset and the source code can be found in our online repository (https://github.com/csehong/SSPP-DAN). Comment: Accepted to ICIP 2017 Oral, Code is available at https://github.com/csehong/SSPP-DAN
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1702.04069 Zobrazit plný text záznamu View this record from Arxiv