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

arXiv: https://arxiv.org/abs/2410.09866 · HTML: https://mlsanity.com/paper/2410.09866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [Correlation and variable importance in random forests](https://mlsanity.com/paper/1310.5726/) (2013)
- [Biometric Presentation Attack Detection: Beyond the Visible Spectrum](https://mlsanity.com/paper/1902.11065/) (2019)
- [Semi-Supervised Spam Detection in Twitter Stream](https://mlsanity.com/paper/1702.01032/) (2017)
- [Thermal Features for Presentation Attack Detection in Hand Biometrics](https://mlsanity.com/paper/1809.04364/) (2018)
- [CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery](https://mlsanity.com/paper/1503.00561/) (2015)

## Abstract (arXiv)

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.