Multi-branch Face Quality Assessment for Face Recognition
文献类型：会议论文
| 作者 | Lijun, Zhang1,2 ; Xiaohu, Shao1,2; Fei, Yang1,2; Pingling, Deng1,2; Xiangdong, Zhou1,2; Yu, Shi1,2
|
| 出版日期 | 2019 |
| 会议日期 | October 16, 2019 - October 19, 2019 |
| 会议地点 | Xi'an, China |
| DOI | 10.1109/ICCT46805.2019.8947255 |
| 页码 | 1659-1664 |
| 英文摘要 | The quality of face images varies due to complex environmental factors, and face images with extremely poor qualities would deteriorate the performance of face recognition. As one of the pre-processing modules of face recognition, face quality assessment needs to consider both environment factors and practical applications. In this paper, we propose a multibranch face quality assessment (MFQA) algorithm considering comprehensive factors acting as a reliable reference for its following recognition. A light-weight convolution neural network (CNN) is used for face image feature extraction, and quality scores corresponding with alignment, visibility, deflection and clarity are predicted by multi-branch layers. Moreover, a score fusion module is implemented by fusing the above scores to obtain a final quality confidence. Compared with other relevant quality assessment works, our method is quite suitable for practical applications because of its better performance, faster speed and smaller model size. Experiments show that our proposed method is able to assess face quality objectively, and the performance of face recognition is significantly improved by introducing our approach into its training and testing procedures. © 2019 IEEE. |
| 会议录 | 19th IEEE International Conference on Communication Technology, ICCT 2019
 |
| 语种 | 英语 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9790]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, China 2.Chongqing Institute of Green and Intelligent Technology, CAS, China; |
| 推荐引用方式 GB/T 7714 | Lijun, Zhang,Xiaohu, Shao,Fei, Yang,et al. Multi-branch Face Quality Assessment for Face Recognition[C]. 见:. Xi'an, China. October 16, 2019 - October 19, 2019. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。