A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IET Biometrics Pub Date : 2024-03-25 DOI:10.1049/2024/3236602

Tuğçe Arıcan, Raymond Veldhuis, Luuk Spreeuwers, Loïc Bergeron, Christoph Busch, Ehsaneddin Jalilian, Christof Kauba, Simon Kirchgasser, Sébastien Marcel, Bernhard Prommegger, Kiran Raja, Raghavendra Ramachandra, Andreas Uhl

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Abstract

Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.

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使用经典方法和深度学习方法进行跨设备手指静脉识别的比较研究

手指静脉识别在生物识别领域越来越受欢迎，但手指静脉模式的互操作性受到的关注却很有限。本研究旨在填补这一空白，它引入了跨设备手指静脉数据集，并使用经典方法、卷积神经网络和我们提出的基于补丁的卷积自动编码器（CAE）评估了跨设备手指静脉识别的性能。研究结果强调了手指静脉识别标准化的重要性，这与指纹或虹膜识别类似，对于实现互操作性至关重要。尽管跨设备识别存在固有的挑战，但本研究中提出的 CAE 架构在手指静脉识别方面，特别是在跨设备比较方面，显示出了良好的效果。

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来源期刊

IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

5.90

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding. The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies: Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.) Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches Soft biometrics and information fusion for identification, verification and trait prediction Human factors and the human-computer interface issues for biometric systems, exception handling strategies Template construction and template management, ageing factors and their impact on biometric systems Usability and user-oriented design, psychological and physiological principles and system integration Sensors and sensor technologies for biometric processing Database technologies to support biometric systems Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection Biometric cryptosystems, security and biometrics-linked encryption Links with forensic processing and cross-disciplinary commonalities Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated Applications and application-led considerations Position papers on technology or on the industrial context of biometric system development Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions Relevant ethical and social issues