CGAN-DA: a cross-modality domain adaptation model for hand-Vein biometric-based authentication - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue JOURNAL OF Cyber-Physical-Social Intelligence Année : 2022

CGAN-DA: a cross-modality domain adaptation model for hand-Vein biometric-based authentication

Résumé

Palm-vein recognition has been the focus of large research efforts over the last years. However, despite the effectiveness of deep learning models, in particular Convolutional Neural Networks (CNNs), in automatically learning robust feature representations, thereby obtaining good accuracy, such good performance is usually obtained at the expense of annotating a large training dataset. Labeling vein images, however, is an expensive and tedious process. Although handcrafted schemes for data augmentation usually increase slightly performance, they are unable to cover complex variations inherently characterizing such images. To overcome this issue, we propose a new unsupervised domain adaptation model, called CycleGAN-based domain adaptation (CGAN-DA), that extracts discriminant representation from the palmvein images, without requiring any image labeling. Our CGAN-DA models allows a conjoint adaptation, at the image and feature levels. Specifically, in order to enhance the extracted features’ domain-invariance, image appearance is transformed across two domains, palm-vein domain and retinal domain. We employ several adversarial losses namely a segmentation loss and a cycle consistence loss to train our model without any annotation from the target domain (palm-vein images). Our experiments on the public CASIA palm-vein dataset demonstrates that our models significantly outperforms the s tart of the art in terms of verification accuracy.
Fichier principal
Vignette du fichier
Yang et al. (2022) - CGAN-DA A Cross-Modality Domain Adaptation Model .pdf (2.2 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04304430 , version 1 (29-11-2023)

Identifiants

Citer

Shuqiang Yang, Yiquan Wu, Xin Jin, Mounim El Yacoubi, Huafeng Qin. CGAN-DA: a cross-modality domain adaptation model for hand-Vein biometric-based authentication. JOURNAL OF Cyber-Physical-Social Intelligence, 2022, 1, pp.3-12. ⟨10.61702/YRMC5182⟩. ⟨hal-04304430⟩
36 Consultations
15 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More