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Efficient disentangled representation learning for multi-modal finger biometrics.
- Source :
-
Pattern Recognition . Jan2024, Vol. 145, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Most multi-modal biometric systems use multiple devices to capture different traits and directly fuse multi-modal data while ignoring correlation information between modalities. In this paper, finger skin and finger vein images are acquired from the same region of the finger and therefore have a higher correlation. To represent data efficiently, we propose a novel Finger Disentangled Representation Learning Framework (FDRL-Net) that is based on a factorization concept, which disentangles each modality into shared and private features, thereby improving complementarity for better fusion and extracting modality-invariant features for heterogeneous recognition. Besides, to capture as much finger texture as possible, we utilize three-view finger images to reconstruct full-view multi-spectral finger traits, which increases the identity information and the robustness to finger posture variation. Finally, a Boat-Trackers-based multi-task distillation method is proposed to migrate the feature representation ability to a lightweight multi-task network. Extensive experiments on six single-view multi-spectral finger datasets and two full-view multi-spectral finger datasets demonstrate the effectiveness of our approach. • FDRL-Net is the first disentangled representation learning method on finger traits. • A direct Boat-Trackers-based multi-task distillation method is proposed. • Extensive experiments shows the superiority of our proposed method. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BIOMETRY
*BIOMETRIC identification
*DISTILLATION
*FACTORIZATION
*POSTURE
*VEINS
Subjects
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 145
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 172778090
- Full Text :
- https://doi.org/10.1016/j.patcog.2023.109944