An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition

Jhong, Sin-Ye; Tseng, Po-Chih; Siriphockpirom, Natnuntnita; Hsia, Chih-Hsien; Huang, Ming-Shih; Hua, Kai-Lung; Chen, Yung-Yao

doi:10.1109/aris50834.2020.9205778

Cited by 17 publications

(10 citation statements)

References 17 publications

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“…In the field of DL, the CNN is the most famous and commonly employed algorithm [30,[71][72][73][74][75]. The main benefit of CNN compared to its predecessors is that it automatically identifies the relevant features without any human supervision [76].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…Jung et al [9] have highlighted the expanding role of automatic BA systems in various domains, encompassing applications such as automated identity verification, information capture, security checks, and protection against identity fraud. As biotechnology advances, the market witnesses the emergence of biometric-based identification systems that demand precision and user-friendliness.…”

Section: Related Workmentioning

confidence: 99%

Palmprint Recognition Using Pre-Trained Convolutional Neural Networks

Ebrahimpour,

Günay

2023

Cognitive Models and Artificial Intelligence Conference Proceedings

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Palmprint recognition (PR) has garnered significant interest due to its distinctive characteristics and potential applications as a robust and secure biometric authentication (BA) method across various domains. This paper introduces a novel approach to enhance PR using pre-trained Convolutional Neural Network (CNN) models. The proposed method process harnesses the capabilities of Deep Learning (DL) and Transfer Learning (TL) to improve palmprint recognition by leveraging state-of-the-art CNN architectures for feature extraction and classification. This proposed method begins by exploring the potential of pre-trained CNN architectures, including ShuffleNet, EfficientNet, and MobileNet, as feature extractors for palmprints. In the next step, extracted feature vectors are compared using the Cosine Similarity (CS) method. The proposed method is thoroughly evaluated through comprehensive experiments. Results of evaluations demonstrate that pre-trained CNN models excel at recognizing palmprints for biometric authentication, establishing their proficiency in this domain. Consequently, this paper illuminates the inherent capabilities of pre-trained CNN models as a potent tool for advancing PR, introducing an innovative facet to BA methodologies.

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“…Jhong et al in [11] present a non-contact hand palm vein solution based on a CNN recognition algorithm that is mention in the following section.…”

Section: ) Acquisition Storage and Processing Hardwarementioning

confidence: 99%

“…A reduced number of CNN architectures have been tested. In 2020, Jhong et al [11] presented a VGG16-inspired solution using contactless images (acquired by the authors) previously enhanced with the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm [17]. The preprocessing step (apart from ROI extraction) is not very common in CNN solutions due to the high recognition performance achieved with raw images, but it could be optimal if the images have poor quality or the ROI has a reduced size.…”

Section: ) Acquisition Storage and Processing Hardwarementioning

confidence: 99%

Deep Learning for Vein Biometric Recognition on a Smartphone

Garcia-Martin

Sánchez-Reillo

2021

IEEE Access

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The ongoing COVID-19 pandemic has pointed out, even more, the important need for hygiene contactless biometric recognition systems. Vein-based devices are great non-contact options although they have not been entirely well-integrated in daily life. In this work, in an attempt to contribute to the research and development of these devices, a contactless wrist vein recognition system with a reallife application is revealed. A Transfer Learning (TL) method, based on different Deep Convolutional Neural Networks architectures, for Vascular Biometric Recognition (VBR), has been designed and tested, for the first time in a research approach, on a smartphone. TL is a Deep Learning (DL) technique that could be divided into networks as feature extractor, i.e., using a pre-trained (different large-scale dataset) Convolutional Neural Network (CNN) to obtain unique features that then, are classified with a traditional Machine Learning algorithm, and fine-tuning, i.e., training a CNN that has been initialized with weights of a pre-trained (different large-scale dataset) CNN. In this study, a feature extractor base method has been employed. Several architecture networks have been tested on different wrist vein datasets: UC3M-CV1, UC3M-CV2, and PUT. The DL model has been integrated on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 smartphones obtaining high biometric performance, up to 98 % of accuracy and less than 0.4 % of EER with a 50-50 % train-test on UC3M-CV2, and fast identification/verification time, less than 300 milliseconds. The results infer, high DL performance and integration reachable in VBR without direct user-device contact, for real-life applications nowadays.

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An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition

Cited by 17 publications

References 17 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Palmprint Recognition Using Pre-Trained Convolutional Neural Networks

Deep Learning for Vein Biometric Recognition on a Smartphone

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