Finger Vein Presentation Attack Detection Based on Texture Analysis


Ashari, Nurul Nabihah and Teng, Jackson Horlick and Ong, Thian Song and Sonai Muthu Anbananthen, Kalaiarasi (2021) Finger Vein Presentation Attack Detection Based on Texture Analysis. In: 7th International Conference on Computational Science and Technology, ICCST 2020, 29 - 30 August 2020, Pattaya, Thailand.

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Biometrics is an effective way to identify and authenticate users based on their personal traits. Among all kinds of hand-based biometrics, finger vein appears to be emerging biometrics that has received a great attention due to its rich information available and ease for implementation. With finger vein system becoming more and more popular, there have been various attempts to comprise the system. Recent studies reveal the vulnerabilities of finger vein system to presentation attack where the sensory device accepts a fake printed finger vein image and gives access as if it were a genuine attempt. In this study, a presentation attack detection method based on hybrid feature spaces of finger vein texture analysis is proposed. Histogram of oriented gradient operator is applied on different channels of grayscale and color feature spaces to obtain texture information of the histogram descriptors. The proposed method includes two implementations of feature space analysis, namely CHOG1 and CHOG 2 . A well-established publicly available dataset is used to analysis and evaluate the proposed implementations. Experimental results suggest that the combination channels of grayscale and color luminance is able to generate better performance through Support Vector Machine classifier with ACER as low as 0.60% and 0.74% for CHOG 1 and CHOG 2 , respectively. The experiments show that the implementation of CHOG 1 performs slightly better than single channel max gradients of CHOG 2 .

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Biometric identification
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 May 2021 13:43
Last Modified: 01 May 2021 13:43


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