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학술대회 Lightweight Deep Embeddings Fusion Methods for Face Verification on Mobile Devices
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저자
김영삼, 조관태, 노종혁, 조상래
발행일
201910
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1133-1136
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8939565
협약과제
19HH3900, 고신뢰 지능정보 서비스에서 휴먼(H)-인프라(I)-서비스(S)를 연결하는 Portal Device 보안 기술 개발, 조상래
초록
With the emergence of deep learning technology, face verification performance on the LFW benchmark outperformed human-level accuracy. In recent, face verification technique is applied to mobile devices to authenticate owner of them. Face verification on mobile devices needs to be considered with both performance and computational cost which is trade-off. In this paper, we propose lightweight network architectures and fusion methods for efficient face verification task on mobile devices. The proposed lightweight networks achieved comparable accuracy on both LFW and YTF datasets with only about 9x fewer size of model and 2x shorter inference time than Inception-resent-v1 which is a deeper network architecture. In addition, proposed score-level fusion method shows improvement of 2.38% on VR@FAR=1e-6 on LFW BLUFR and that of 0.28% on accuracy on YTF than single lightweight network.
키워드
CNN, Deep learning, Face verification, Fusion.
KSP 제안 키워드
Mobile devices, Network Architecture, Trade-off, computational cost, deep learning(DL), face verification, fusion method, learning technology, score-level fusion