Secure Authentication for Face Recognition

Abstract

In this paper, we present a new technique to protect the face biometric during recognition, using the so called cancellable biometric. The technique is based on image-based (statistical) face recognition using the 2DPCA algorithm. The biometric data is transformed to its cancellable domain using polynomial functions and co-occurrence matrices. Original facial images are transformed non-linearly by a polynomial function whose parameters can be change accordingly to the issuing version of the secure cancellable template. Co-occurrence matrices are also used in the transform to generate a distinctive feature vector which is used for both security and recognition accuracy. The Hadamard product is used to construct the final cancellable template. It shows high flexibility in proving a new relationship between two independent covariance matrices, which is mathematically proven. The generated cancellable templates are used in the same fashion as the original facial images. The 2DPCA recognition algorithm has been used without any changes; the transformations are applied on the input images only and yet with higher recognition accuracy. Theoretical and experimental results have shown high irreversibility of data with improved accuracy of up to 3% from the original data