Class-Distribution Preserving Transform for Face Biometric Data Security

This paper addresses the face data variations problem in biometric cryptosystems in which the cryptographic technique is applied to biometric system. To overcome the limitation, this paper introduce a new class-distribution preserving transform to biometric cryptosystems. The basic idea is to transform a real value face feature vector to a binary feature vector using a random points set. The proposed transform is integrated into a BCH coding technique. Fisherface algorithm is used for feature extraction and ORL face database is selected for experiments. It is shown that only around 0.8% accuracy is degraded in comparing with the original Fisherface algorithm while the system security can be enhanced by 126 bits.