LEFV: A Lightweight and Efficient System for Face Verification with Deep Convolution Neural Networks
Pages 222 - 227
Abstract
The emergence of deep learning has made great progress in face recognition. With the popularization of embedded devices, deploying the deep model on embedded devices has become a trend. Most high-precision models require lots of computation costs. Therefore, developing a lightweight deep face recognition system running on embedded devices is a hot topic in current research. To achieve high-accuracy real-time performance of an embedded device, we now present a simple and effective face recognition system LEFV, including face detection, face normalization and face recognition. The quantitative experiments on two large-scale challenging datasets, WIDER FACE dataset and IJB-A dataset, show competitive performances on both runtime and accuracy.
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Index Terms
- LEFV: A Lightweight and Efficient System for Face Verification with Deep Convolution Neural Networks
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Published In
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- Xidian University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2020
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ICVIP 2019
ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing
December 20 - 23, 2019
Shanghai, China
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