Towards Secure Biometric Solutions: Enhancing Facial Recognition while Protecting User Data

This paper presents a novel approach to the storage of facial images in databases designed for biometric authentication, with a primary focus on user privacy. Biometric template protection encompasses a variety of techniques aimed at safeguarding users’ biometric information. Generally, these methods involve the application of transformations and distortions to sensitive data. However, such alterations can frequently result in diminished accuracy within recognition systems. We propose a deformation process to generate temporary codes that facilitate the verification of registered biometric features. Subsequently, facial recognition is performed on these registered features in conjunction with new samples. The primary advantage of this approach is the elimination of the need to store facial images within application databases, thereby enhancing user privacy while maintaining high recognition accuracy. Evaluations conducted using several benchmark datasets including AgeDB-30, CALFW, CPLFW, LFW, RFW, XQLFW- demonstrate that our proposed approach pre serves the accuracy of the biometric system. Furthermore, it mitigates the necessity for applications to retain any biometric data, images, or sensitive information that could jeopardize users’ identities in the event of a data breach. The solution code, benchmark execution, and demo are available at: https://bc1607.github.io/FRS-ProtectingData.