Cancelable face template protection using transform features for cyberworld security

Cyber world becomes a fundamental and vital component of the physical world with the increase of dependence on internet-connected devices in industry and government organizations. Provision of privacy and security of users during online communication offers unique cybersecurity challenges for industry and government. Intrusion is one of the crucial issues of cybersecurity, which can be overcome by providing the vigorous authentication solutions. Biometrics authentication is used in different cybersecurity systems for user authentication purpose. The cancelable biometric is a solution to rid of privacy problems in traditional biometric systems. This paper purposes a new cancelable face authentication method, which uses Hybrid Gabor PCA (HGPCA) descriptor for cyberworld security. The proposed method uses the wavelet transform for the extraction of the features of the face images by using Gabor filter and Principal Component Analysis (PCA). Later, both types of features have been ensemble by using the simple concatenation scheme. Then scrambling has been applied to the fused features by using the random key generated by the user. So finally, scrambled fused features has been stored in the database which are used for the cancelable biometric authentication as well as recovery. HGPCA achieves "cancelability" and increases the authentication accuracy. The proposed method has been tested on three standard face datasets. Experimental results of the proposed method have been compared with existing methods by using standard quantitative measures that show superiority over existing methods.