Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection

Bera, Asish; Bhattacharjee, Debotosh; Shum, Hubert P.H.

Abstract

This paper presents a human verification scheme in two independent stages to overcome the vulnerabilities of attacks and to enhance security. At the first stage, a hand image-based CAPTCHA (HandCAPTCHA) is tested to avert automated bot-attacks on the subsequent biometric stage. In the next stage, finger biometric verification of a legitimate user is performed with presentation attack detection (PAD) using the real hand images of the person who has passed a random HandCAPTCHA challenge. The electronic screen-based PAD is tested using image quality metrics. After this spoofing detection, geometric features are extracted from the four fingers (excluding the thumb) of real users. A modified forward–backward (M-FoBa) algorithm is devised to select relevant features for biometric authentication. The experiments are performed on the Boğaziçi University (BU) and the IIT-Delhi (IITD) hand databases using the k-nearest neighbor and random forest classifiers. The average accuracy of the correct HandCAPTCHA solution is 98.5%, and the false accept rate of a bot is 1.23%. The PAD is tested on 255 subjects of BU, and the best average error is 0%. The finger biometric identification accuracy of 98% and an equal error rate (EER) of 6.5% have been achieved for 500 subjects of the BU. For 200 subjects of the IITD, 99.5% identification accuracy, and 5.18% EER are obtained.

Citation

Bera, A., Bhattacharjee, D., & Shum, H. P. (2021). Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection. Expert Systems with Applications, 171, https://doi.org/10.1016/j.eswa.2021.114583

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2021
• Online Publication Date: Jan 9, 2021
• Publication Date: Jun 1, 2021
• Deposit Date: Mar 23, 2021
• Publicly Available Date: Jan 9, 2022
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 171
• DOI: https://doi.org/10.1016/j.eswa.2021.114583
• Public URL: https://durham-repository.worktribe.com/output/1245161