Rotating face detection based on convergent cascaded convolutional neural network

被引：0
|
作者：
Qi Y. [1 ]
Dong Y. [2 ]
Wang Y. [3 ]
机构：
[1] Computer Network Center, Taiyuan Open University, Taiyuan
[2] College of Software, Taiyuan University of Technology, Taiyuan
[3] Department of Information Technology and Engineering, Jinzhong University, Jinzhong
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2022年 / 51卷 / 12期
关键词：
CNN; parallel cascade; rotating face detection; rotation-in-plane; scale transformation;
D O I：
10.3788/IRLA20220176
中图分类号：
学科分类号：
摘要：
To solve the problem of low accuracy of multi-scale rotating face detection under complex conditions such as large-scale pose change and large-angle face rotation-in-plane, a rotating face detection method based on parallel cascade convolution neural network is proposed. Using a coarse-to-fine cascading strategy, multiple shallow convolutional neural networks are cascaded in parallel on multiple feature layers of the main network SSD. Face/non-face detection, face boundary box position update and face RIP angle estimation are gradually completed. Experimental results on Rotate FDDB dataset and Rotate Sub-WIDER FACE dataset show that the proposed method achieves advanced face detection. The detection precision of the method is 87.1% and the speed is 45 FPS when 100 false positives occur in the rotating Sub-WIDER FACE dataset, which proves that the method can achieve accurate rotating face detection with low time loss. © 2022 Chinese Society of Astronautics. All rights reserved.
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