Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Abstract

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics.

Keywords

• cancelable biometrics
• wavelet transformations
• chaotic maps
• deep learning