S-DCTNet: Security-oriented biometric feature extraction technique

The proliferation of information technology has prompted researchers to create a multitude of new security solutions for secure electronic applications, especially on the Internet. Among them, security officials prefer authentication systems for user’s identity identification. Indeed, biometric authentication has proved to be superior in many respects compared to the traditional authentication means. Unfortunately, these systems are vulnerable to a variety of attacks, the most serious of which is perhaps the attack on the stored or transmitted template, which makes the safety of this template more important in the design of the biometric systems. This research, therefore, suggests an effective feature extraction method that can provide a deep and cancelable biometric feature. In this study, DCTNet deep learning is combined with chaotic systems to extract revocable palmprint/palm-vein features.