计算机科学
联营
面部识别系统
人工智能
串联(数学)
面子(社会学概念)
特征(语言学)
棱锥(几何)
模式识别(心理学)
光学(聚焦)
比例(比率)
机器学习
光学
社会学
哲学
物理
组合数学
量子力学
语言学
社会科学
数学
作者
Zhaohua Peng,Feng Zhao,Peng Liu,Mengwei Li
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 31740-31750
被引量:10
标识
DOI:10.1109/access.2022.3150862
摘要
Although face recognition has achieved great success due to deep learning, many factors may affect the quality of faces in the wild, such as pose changes, age variations, and light changes, which can seriously affect the performance of face recognition. In this work, an effective approach called Efficient Lightweight Attention Networks (ELANet) is proposed to address the challenge brought by the impacts of poses and ages on face recognition performance. First, similar local patches are particularly important when the geometry and appearance of a face change drastically. To alleviate this challenge, spatial attention is used to capture important locally similar patches and channel attention is employed to focus on features with different levels of importance. Furthermore, Efficient Fusion Attention (EFA) module is designed to achieve better performance, which can alleviate the computational effort required by fusing spatial and channel attention. Second, multi-scale features learning is necessary because pose or large expression changes can cause similar recognition regions to appear at different scales. For this purpose, pyramid multi-scale module is presented, which constructs a series of features at different scales via pooling operations. Third, to unite low-level local detail information with high-level semantic information, the features of different layers are fused by Adaptively Spatial Feature Fusion (ASFF) instead of simply utilizing addition or concatenation. Compared to recent lightweight networks, the ELANet improved performance by 1.83% and 2.17% on the CPLFW and VGG2_FP datasets, respectively, and by 0.92% on the CALFW dataset. The ELANet addresses the challenge regarding the impacts of poses and ages on face recognition performance with few parameters and computational effort and is suitable for embedded and mobile devices.
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