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In the field of deep learning, understanding the rationale behind an automatic system’s decisions is essential for building users’ trust and ensuring accountability. In this regard, explainable artificial intelligence (XAI) recently emerged as a valuable tool to offer insights into a model behavior. The present study focuses on vein-based biometric recognition, investigating techniques allowing to identify which regions of a wrist-vein image are mostly exploited to carry out a verification process. Toward this aim, our research exploits vision transformers (ViTs), which rely on self-attention mechanisms to automatically detect and exploit the input parts with the content deemed most relevant for its further processing. Two distinct wrist-vein pattern datasets, namely PUT-wrist and FYO-wrist, are employed to fine-tune the considered models. Their behavior is interpreted by analyzing the attention maps generated when applying the trained networks to vein-pattern images, investigating which regions are exploited to decide a user’s identity. The proposed approach testifies that the performed recognition process can improve when a ViT focuses on areas with significant vein pattern content, achieving verification performance surpassing state-of-the-art methods in open-set scenarios, while promoting transparency through explainability.

In recent years, vein-based biometric recognition has received ever-increasing attention from both academia and industry, due to the advantages it offers over traditional biometric traits such as fingerprint, iris, and face. Nonetheless, some issues related to the use of vein biometrics still need to be investigated and understood. Specifically, in this study, we speculate about the gender-related variations in vein patterns, and their effects on biometric verification performance. An analysis on the feasibility of recognizing male and female subjects depending on their hand-vein patterns, and on the level of similarity characterizing the biometric templates extracted from male and female populations, are here carried out considering three different databases. Specifically, the public VERA dataset, containing samples of palm-vein patterns, and two datasets containing images of finger-vein patterns, i.e., the UTFVP public database, and an in-house dataset collected with an on-the-move contactless modality, are here considered. The obtained experimental results show that the approach here proposed to perform gender recognition allows to reach an accuracy up to 95.83% on the public finger-vein UTFVP dataset, and to outperform the current state-of-the-art on the public palm-vein VERA dataset, with accuracy at 93.55%. It is also shown that vein-based biometric systems can benefit from the exploitation of information regarding the gender of the considered subjects, with achievable recognition rates that can be significantly improved by designing a biometric verification system relying on gender-specific models for extracting the employed discriminative templates.

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems. [ABSTRACT FROM PUBLISHER]