照明对手指血管模式识别的影响

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pesigrihastamadya Normakristagaluh, Geert J. Laanstra, Luuk J. Spreeuwers, Raymond N. J. Veldhuis
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引用次数: 0

摘要

本文研究了光照方向和光束宽度对手指血管图案成像和识别性能的影响。本文提出了一个定性理论模型来解释手指血管在皮肤上的投影。使用我们设计的扫描仪进行了一系列实验,实验中采用了从顶部、单方向(左侧或右侧)、窄光束或宽光束进行照明。实验收集了一个新的数据集,其中包含 4428 张近红外图像,这些图像是在控制良好的条件下采集的手指静脉图案,以最大限度地减少不同环节之间的位置和旋转角度差异。顶部照明表现良好,因为它更均匀,能增强更多的可见静脉。较窄的光束不会影响哪些静脉可见,但会减少手指边界的过度曝光,提高血管图案图像的质量。窄光束的性能最佳,FNMR@FMR0.01%,而宽光束则始终导致较高的错误不匹配率。左侧和右侧照明对比的错误率最高,因为在两幅图像中都只能看到手指中间的静脉。不同方向的照明可能是互通的,因为它们产生相同的血管模式,主要是手指表面的投影阴影。右侧和左侧的评分和图像融合结果与顶部照明下的识别性能相似,表明静脉图案与照明方向无关。所有这些实验结果都支持所提出的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Impact of Illumination on Finger Vascular Pattern Recognition

The Impact of Illumination on Finger Vascular Pattern Recognition

This paper studies the impact of illumination direction and bundle width on finger vascular pattern imaging and recognition performance. A qualitative theoretical model is presented to explain the projection of finger blood vessels on the skin. A series of experiments were conducted using a scanner of our design with illumination from the top, a single-direction side (left or right), and narrow or wide beams. A new dataset was collected for the experiments, containing 4,428 NIR images of finger vein patterns captured under well-controlled conditions to minimize position and rotation angle differences between different sessions. Top illumination performs well because of more homogenous, which enhances a larger number of visible veins. Narrower bundles of light do not affect which veins are visible, but they reduce the overexposure at finger boundaries and increase the quality of vascular pattern images. The narrow beam achieves the best performance with 0% of [email protected]%, and the wide beam consistently results in a higher false nonmatch rate. The comparison of left- and right-side illumination has the highest error rates because only the veins in the middle of the finger are visible in both images. Different directional illumination may be interoperable since they produce the same vascular pattern and principally are the projected shadows on the finger surface. Score and image fusion for right- and left-side result in recognition performance similar to that obtained with top illumination, indicating the vein patterns are independent of illumination direction. All results of these experiments support the proposed model.

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来源期刊
IET Biometrics
IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
0.00%
发文量
46
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
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