An Efficient and Accurate Face Verification Method Based on CNN Cascade Architecture

DOI

10.2991/caai-17.2017.120How to use a DOI?

Keywords

face verification; deep learning; convolution neural network; metric learning

Abstract

Unconstrained face verification has been actively studied for decades in computer vision. Recent algorithms rely on Convolution Neural Network to further improve the accuracy. However, such algorithms tend to be time-consuming and computationally complex, which cannot meet the real-time requirements. In this paper, we propose an efficient and accurate face verification method based on Convolution Neural Network Cascade architecture. First, we use a compact network to handle most of the simple samples. Then, we use a complex network to handle a small number of hard samples. Finally, we use an ensemble of multi-patch networks with metric learning. Our method achieves an accuracy of 99.72% on LFW, which performs favorably against the state-of-the-arts. Furthermore, we significantly reduce time cost from 485ms to only 20ms on a single core i7-4790, which has strong practical value for real-time face verification systems.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dangdang Chen
AU  - Lanqing He
AU  - Shengming Yu
AU  - Shengjin Wang
PY  - 2017/06
DA  - 2017/06
TI  - An Efficient and Accurate Face Verification Method Based on CNN Cascade Architecture
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 534
EP  - 540
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.120
DO  - 10.2991/caai-17.2017.120
ID  - Chen2017/06
ER  -