Deep Auto-Encoding and Biohashing for Secure Finger Vein Recognition

Abstract

Biometric recognition systems relying on finger vein have gained a lot of attention in recent years. Besides security, the privacy of finger vein recognition systems is always a crucial concern. To address the privacy concerns, several biometric template protection (BTP) schemes are introduced in the literature. However, despite providing privacy, BTP algorithms often affect the recognition performance. In this paper, we propose a deep-learning-based approach for secure finger vein recognition. We use a convolutional auto-encoder neural network with a multi-term loss function. In addition to the auto-encoder loss function, we deploy triplet loss for the embedding features. Next, we apply Biohashing to our deep features to generate protected templates. The experimental results indicate that the proposed method achieves superior performance to previous finger vein recognition methods protected with Biohashing. Besides, our proposed method has less execution time and requires less memory. 1

Biometric Template Attacks and Recent Protection Mechanisms: A Survey

“…With the recent adoption of deep learning in almost every computing and information-processing domain, BTP researchers have also leveraged the trend to achieve non-invertibility, as proposed in the works of Shahreza and Marcel [109]. The authors introduce a secure finger vein detection method based on deep learning, which is achieved using a neural network with a convolutional auto-encoder and a multi-term loss function.…”

FV-DMHN: Dual Multi-Head Network for Finger Vein Recognition

“…Yang et al [17] used a generative adversarial network to learn the feature representation of finger veins. vein feature representation, Shahreza et al [18] used an autoencoder to learn the feature labeling of finger veins. Qin et al [19]introduced deep learning models for finger vein quality assessment.…”