Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
| Autor: | Hitoshi Imaoka, Akinori F. Ebihara, Takaya Miyamoto, Akihiro Hayasaka, Hiroshi Hashimoto |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Deep learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition Facial recognition system Domain (software engineering) Machine Learning (cs.LG) Discriminative model Hfr cell Feature (machine learning) Task analysis Artificial intelligence business Representation (mathematics) |
| Zdroj: | IJCB |
| Popis: | Face recognition for visible light (VIS) images achieve high accuracy thanks to the recent development of deep learning. However, heterogeneous face recognition (HFR), which is a face matching in different domains, is still a difficult task due to the domain discrepancy and lack of large HFR dataset. Several methods have attempted to reduce the domain discrepancy by means of fine-tuning, which causes significant degradation of the performance in the VIS domain because it loses the highly discriminative VIS representation. To overcome this problem, we propose joint feature distribution alignment learning (JFDAL) which is a joint learning approach utilizing knowledge distillation. It enables us to achieve high HFR performance with retaining the original performance for the VIS domain. Extensive experiments demonstrate that our proposed method delivers statistically significantly better performances compared with the conventional fine-tuning approach on a public HFR dataset Oulu-CASIA NIR&VIS and popular verification datasets in VIS domain such as FLW, CFP, AgeDB. Furthermore, comparative experiments with existing state-of-the-art HFR methods show that our method achieves a comparable HFR performance on the Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance. 8 pages, 5 figures. Accepted at IJCB 2021 |
| Databáze: | OpenAIRE |
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