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Attentional Feature-Pair Relation Networks for Accurate Face Recognition
- Source :
- ICCV
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.<br />To appear in ICCV 2019
- Subjects :
- FOS: Computer and information sciences
Relation (database)
Biometrics
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
Pattern recognition
02 engineering and technology
010501 environmental sciences
01 natural sciences
Facial recognition system
Expression (mathematics)
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
020201 artificial intelligence & image processing
Artificial intelligence
business
0105 earth and related environmental sciences
Block (data storage)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Accession number :
- edsair.doi.dedup.....c1dec60e1340032a00abadf97023502b