A Multimodal Biometric Recognition System Based on Palmprint and Dorsal Hand Vein Using DenseNet
| dc.contributor.author | Priyanka | |
| dc.date.accessioned | 2026-09-23T05:56:42Z | |
| dc.date.available | 2026-09-23T05:56:42Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Multimodal biometric systems have emerged as an effective solution to overcome the limitations of unimodal systems, such as susceptibility to noise, spoofing, and limited discriminative capability. Among various biometric traits, palmprint and dorsal hand vein modalities provide high distinctiveness and reliability. In particular, dorsal hand vein patterns, being subcutaneous, offer enhanced resistance to forgery and environmental variations. This thesis is a multimodal biometric recognition system that implement palm print and dorsal hand vein images using deep learning based feature ex- traction techniques. Different fusion Methods such as feature, score, and hybrid fusion, are evaluated to determine the best approach. Experimental results shows that hybrid fusion achieves best performance by effectively combining score and feature fusion. Furthermore, a Siamese DenseNet model with triplet loss and arcFace loss is employed to learn discriminative embeddings and acheived an Area Under Curve (AUC) of 0.991 and an Equal Error Rate (EER) of 0.045%. The results highlight the effectiveness of combining multiple biometric modalities with advanced fusion techniques for robust authentication. | |
| dc.identifier.uri | http://nits.ndl.gov.in/handle/123456789/120 | |
| dc.language.iso | en | |
| dc.publisher | National Institute of Technology, Silchar | |
| dc.title | A Multimodal Biometric Recognition System Based on Palmprint and Dorsal Hand Vein Using DenseNet |