Paper Info
Title
Gabor Log-Euclidean Gaussian and its fusion with deep network based on self-attention for face recognition
Abstract
In this work, we proposed a face feature extraction method by Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component Analysis (called LGLG-WPCA). The proposed method extracts raw features from the multivariate Gaussian in the transform domain of Gabor wavelet and uses WPCA to get robust features. Because the space of Gaussian is a Riemannian manifold, it is difficult to incorporate the learning mechanism into the model. To address this issue, Log-Euclidean approach is used to embed the multivariate Gaussian into the linear space, and then use WPCA to learn discriminative face features. LGLG-WPCA is good at extracting the detail features of face image. Furthermore, another outstanding advantage of LGLG is that its features can be effectively integrated with the high-level features of deep learning network for face recognition in more complex environments. We presented the feature fusing approaches for face recognition based on Self-attention Network (SAN) and achieved obvious performance improvement to the-state-of-the-art deep networks including SENet and FaceNet. Experiments show the proposed method is robust under adverse conditions such as varying poses, skin aging and uneven illumination, and it is suitable for face image under small-scale datasets in complex environments, such as network-based or video-based person searching or tracking. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2022
10.1016/j.asoc.2021.108210
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Face recognition, Gabor wavelet, Log-Euclidean Gaussian, Self-attention, Whitening principal component analysis
Journal
116
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Chaorong Li100.34
Wei Huang200.34
Yuanyuan Huang3275.77