Hand Biometric Verification with Hand Image-Based CAPTCHA

Abstract

An approach for hand biometric recognition with the hand image-based CAPTCHA verification is presented in this paper. A new method for CAPTCHA generation is implemented based on the genuine and fake hand images which are embedded in a complex textured color background image. The HandCaptcha is a useful application to differentiate between the human and automated scripts. The first level of security is achieved by the HandCaptcha against the malicious threats and attacks. After solving the HandCaptcha correctly, the identity of a person is authenticated based on the contact-less hand geometric verification approach in the second level. A set of 300 unique HandCaptcha is created randomly and solved by at least 100 persons with the accuracy of 98.34%. Next, the left-hand images of the legitimate users are normalized, and sixteen geometric features are computed from every normalized hand. Experiments are conducted on the 200 subjects of the Bosporus left-hand database. Classification accuracy of 99.5% has been achieved using the kNN classifier, and the equal error rate is 3.93%.