Paper Info
Title
RGB-D Face Recognition via Deep Complementary and Common Feature Learning
Abstract
RGB-D face recognition has attracted increasing attentions in recent years because of its robustness in unconstrained environment. However, existing approaches either handle individual modalities using completely separate pipelines or treat all the modalities equally using the same pipeline. Such approaches did not adequately consider the modality differences and exploit the modality correlations. We propose a novel approach for RGB-D face recognition that is able to learn complementary features from multiple modalities and common features between different modalities. Specifically, we introduce a joint loss taking activation from both modality-specific feature learning networks, and enforcing the features to be learned in a complementary way. We further extend the capability of this multi-modality (e.g., RGB-D vs. RGB-D) matcher into cross-modality (e.g., RGB vs. RGB-D) scenarios by learning a common feature transformation mapping different modalities into the same feature space. Experimental results on a number of public RGB-D face databases (e.g., EURECOM, VAP, IIIT-D, and BUAA), and a large RGB-D database we collected, show the impressive performance of the proposed approach.
Year
DOI
Venue
2018
10.1109/FG.2018.00012
2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
Keywords
Field
DocType
RGB D face recognition,complementary feature learning,common feature learning,joint loss,cross-modality
Modalities,Facial recognition system,Feature vector,Pattern recognition,Multiple modalities,Computer science,Robustness (computer science),Exploit,RGB color model,Artificial intelligence,Feature learning
Conference
ISSN
ISBN
Citations 
2326-5396
978-1-5386-2336-7
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hao Zhang11034.63
Hu Han275240.02
Jiyun Cui320.71
Shiguang Shan46322283.75
Xilin Chen56291306.27