Failure of affine‐based reconstruction attack in regenerating vascular feature points

Abstract

Abstract Inverse biometrics methods are a major privacy concern for users of biometric recognition systems. Affine‐based reconstruction attack is an inverse biometrics method that models the biometric recognition algorithm by an affine approximation. This type of attack reconstructs targeted biometric references using the modelled biometric recognition algorithm and the comparison scores issued by the system. Although this reconstruction method has only been successfully applied to reconstruct face images, the common consensus is that any biometric system that issues comparison scores could be vulnerable to such an attack since this method is sufficiently general to be applied to other biometric templates. Here it is shown that the attack fails to regenerate sparse vascular feature point templates. The reconstruction attack on feature point patterns extracted from retina and hand vascular images is tested. The inverse attack match rate for reconstructed reference templates was 0.3% in one experiment using retinal vasculature and 0% for all others. These results show that the reconstruction attack is not as catastrophic as it is widely accepted to be, and that vascular biometric template protection schemes that store sparse templates as references and reveal comparison scores are not susceptible to affine‐based reconstruction attacks.

Keywords

• biometrics (access control)
• data privacy
• eye
• face recognition
• feature extraction
• image reconstruction