Two-Stage Human Verification using HandCAPTCHA and Anti-Spoofed Finger Biometrics with Feature Selection

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 Bogazici 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.