使用深度学习改进非接触式指纹成像中的指尖分割

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Laurenz Ruzicka, Bernhard Kohn, Clemens Heitzinger
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引用次数: 0

摘要

生物特征识别系统,特别是利用指纹的系统,由于其可靠性和唯一性,已成为验证用户身份的重要手段。最近转向非接触式指纹传感器需要精确的指尖分割随着背景的变化,以保持高精度。为了提高非接触式指纹图像的分割精度和推理速度,本研究引入了一种结合ResNeSt和une++架构的新型深度学习模型fingerune++。我们的模型明显优于传统和最先进的方法,实现了卓越的性能指标。广泛的数据扩充和优化的模型架构增强了其鲁棒性和效率。这一进步有望提高非接触式生物识别系统在各种实际应用中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

Biometric identification systems, particularly those utilizing fingerprints, have become essential as a means of authenticating users due to their reliability and uniqueness. The recent shift towards contactless fingerprint sensors requires precise fingertip segmentation with changing backgrounds, to maintain high accuracy. This study introduces a novel deep learning model combining ResNeSt and UNet++ architectures called FingerUNeSt++, aimed at improving segmentation accuracy and inference speed for contactless fingerprint images. Our model significantly outperforms traditional and state-of-the-art methods, achieving superior performance metrics. Extensive data augmentation and an optimized model architecture contribute to its robustness and efficiency. This advancement holds promise for enhancing the effectiveness of contactless biometric systems in diverse real-world applications.

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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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