Deep Learning-Based Wrist Vascular Biometric Recognition

Marattukalam, Felix; Abdulla, Waleed H.; Cole, David; Gulati, Pranav

doi:10.3390/s23063132

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In 2023A study presented a novel, cost-effective, end-toend contactless system for wrist vein biometric detection based on deep learning [13]. They used the FYO wrist vein dataset to train a unique U-Net CNN structure, which successfully extracted and segmented wrist vein patterns.…”

Section: Related Workmentioning

confidence: 99%

Proposed Convolutional Neural Network Model for Finger Vein Image Classification

Alhadethy,

Smaoui,

Fakhfakh

et al. 2024

RIA

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The identification of individuals through finger vein patterns has become a prominent biometric technique due to its non-invasiveness and uniqueness. Convolutional neural networks (CNNs) have been at the forefront of this technology, offering impressive recognition rates within large, labeled datasets. Despite their successes, the application of CNNs to finger vein recognition remains a challenging task, largely due to the high dimensionality of input data and the multitude of classification outputs required. This paper presents an optimized CNN model designed to address the intricacies of finger vein image classification. It is posited that increasing the number of feature extraction layers, coupled with a strategic selection of kernel sizes for each layer, significantly enhances model accuracy. Through a series of systematic experiments, the optimal layer configurations were identified, resulting in an architecture that surpasses previous models in classification precision. The proposed CNN architecture demonstrates a classification accuracy exceeding 99%, an improvement over existing method. It is noteworthy that the development of this model has been constrained by the limited scale of current finger vein databases, which poses risks of overfitting. Hence, the expansion of these databases is suggested as a future avenue to reinforce the robustness of the training process. The results depicted in this study underscore the potential of deep learning techniques in biometric security, with the advanced CNN model setting a new benchmark in finger vein recognition.

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Section: Related Workmentioning

confidence: 99%

Proposed Convolutional Neural Network Model for Finger Vein Image Classification

Alhadethy,

Smaoui,

Fakhfakh

et al. 2024

RIA

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“…Closed-set conditions were also considered in [34], where a lightweight network was designed to extract discriminative wrist-vein features, achieving EERs at 1.2% on PUT and 1.84% on FYO when testing on the same subjects employed to train the used model. Open-set verification conditions have been instead considered in [35], where a siamese approach was employed to train a CNN, achieving an F1 score at 84.7%. Also, ViTs were applied to wrist-vein images, as in [20] where an accuracy at 99.5% on PUT was obtained in identification, yet not investigating any explainability aspect nor the generalizability of ViT on verification tasks.…”

Section: Related Workmentioning

confidence: 99%

Explainable Vision Transformers for Vein Biometric Recognition

Albano,

Giusti,

Maiorana

et al. 2024

IEEE Access

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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 finetune 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.

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“…As in other fields, a huge investment has been made to gather a lot of data, use them, discover patterns, and develop helpful models. As mentioned in Section 4.2 , this would be useful not only in DD but also in emotion recognition [ 143 ], human activity recognition [ 144 ], lesion detection and classification [ 145 ], plant classification [ 146 ], biometric recognition [ 147 ], and even in dental care through caries recognition on X-ray images [ 148 ]. The available data therefore include various types of data such as images, biometrics, molecule representation, vocal recording, and so on.…”

Section: Ai and Ddmentioning

confidence: 99%

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

Crouzet,

Lopez,

Riss Yaw

et al. 2024

Molecules

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The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein–protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

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Deep Learning-Based Wrist Vascular Biometric Recognition

Cited by 5 publications

References 30 publications

Proposed Convolutional Neural Network Model for Finger Vein Image Classification

Proposed Convolutional Neural Network Model for Finger Vein Image Classification

Explainable Vision Transformers for Vein Biometric Recognition

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

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