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學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Subject: search.filters.subject.biometrics

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    Evaluating and selecting the biometrics in network security
    (2015-03-25)
    Liu, Che-Hung
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    Wang, Jen-Sheng
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    Peng, Chih-Chiang
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    Shyu, Joseph Z.
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    科技管理研究所
    Since Apple merged with AuthenTec, a leading fingerprint recognition company, in 2012, biometrics has widely been considered to strengthen security and privacy in the network security field. Although biometrics has been applied in specific areas for decades, it has gradually proliferated in customer and mobile electronic products to enhance security and privacy. This study aims to evaluate biometrics through conventional technology assessment considerations combined with viewpoints on the specifics of biometric technologies and then to provide suggestions for selection. To conduct the biometric technology assessment, the fuzzy analytic hierarchy process and non-fuzzy best performance approaches are used. Although the outcomes first indicate that technology assessment should be the key object in selecting biometric technologies, that object is followed by biometric competence and key elements of biometrics. The outcomes also indicate that features of the target technologies should be considered when evaluating them. Additionally, fingerprint recognition, iris recognition, and face recognition are the preferred biometrics in evaluation and selection. Copyright (c) 2014 John Wiley & Sons, Ltd.
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    Test of the Practicality and Feasibility of EDoF-Empowered Image Sensors for Long-Range Biometrics
    (2016-12-01)
    Hsieh, Sheng-Hsun
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    Li, Yung-Hui
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    Tien, Chung-Hao
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    光電工程學系
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    Department of Photonics
    For many practical applications of image sensors, how to extend the depth-of-field (DoF) is an important research topic; if successfully implemented, it could be beneficial in various applications, from photography to biometrics. In this work, we want to examine the feasibility and practicability of a well-known "extended DoF" (EDoF) technique, or "wavefront coding," by building real-time long-range iris recognition and performing large-scale iris recognition. The key to the success of long-range iris recognition includes long DoF and image quality invariance toward various object distance, which is strict and harsh enough to test the practicality and feasibility of EDoF-empowered image sensors. Besides image sensor modification, we also explored the possibility of varying enrollment/testing pairs. With 512 iris images from 32 Asian people as the database, 400-mm focal length and F/6.3 optics over 3 m working distance, our results prove that a sophisticated coding design scheme plus homogeneous enrollment/testing setups can effectively overcome the blurring caused by phase modulation and omit Wiener-based restoration. In our experiments, which are based on 3328 iris images in total, the EDoF factor can achieve a result 3.71 times better than the original system without a loss of recognition accuracy.
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    Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition
    (2018-10-09)
    Chan, Hui-Ling
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    Kuo, Po-Chih
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    Cheng, Chia-Yi
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    Chen, Yong-Sheng
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    交大名義發表
    The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.