On Deep Learning for Dorsal Hand Vein Recognition

The use of biometrics has been one of the most effective solutions for a person's identification and verification. Traditional biometric modalities such as fingerprint, iris, and face recognition have been successfully employed and have shown tremendous success in providing a secure access mechanism. On top of that, the success of deep learning algorithms has showcased that automated biometrics recognition has the potential of surpassing human-level accuracy. Another relatively unexplored biometric modality namely Dorsal Hand Vein (DHV) recently has gained traction in the industry and among researchers from academia. In this paper, we have designed an end-to-end pipeline for DHV biometric authentication that includes image enhancement, region of interest (ROI) extraction, and finally deep learning models for DHV recognition. Three deep learning models namely a custom convolutional neural network (CNN), a Siamese network, and a Triplet Network are trained on publicly available images of DHV datasets. Later, these models are used as feature extractors and tested on images of unseen subjects for authentication. We find that the simple CNN model learns a better feature representation than the Triplet network, which outperforms the Siamese network. One potential reason for such behavior is the limited availability of the datasets used in training.