Binary biometric template generation towards security and class separability

Due to the wide usage of biometrics, its security issues deserve more attention. Many of biometric protection systems require the biometric templates to be presented in a binary form. Therefore, extracting binary templates from real-valued biometric data is a key step in biometric data protection systems. In addition to meeting the security and privacy requirements, binary biometric templates allow fast matching and reduced storage. The main challenge of these approaches is how to convert the real-valued templates into corresponding binary representation which retains the original information. In this paper, we present a novel method that employs Genetic Algorithms(GA)to generate a binarization scheme which used to transform the real-valued templates into robust binary ones. The main role of GA is to search for the optimal quantization and encoding parameters to generate the binarization scheme. Experiments were conducted with ORL face database for recognition. Our results demonstrated that binary templates achieved promising performance in terms of equal error rate for face recognition using a simple hamming distance classifier.