A discriminant binarization transform using genetic algorithm and error-correcting output code for face template protection

In a biometric cryptosystem, biometric templates are encrypted before being stored in the database. This approach often requires a binarization phase to transform the original real-valued templates into their binary versions. Most of the existing binarization schemes do not take into consideration the need to discriminate binary templates; which in turn degrade the system recognition accuracy. This paper proposes a new binarization transform for face template protection using Error-Correcting Output Code (ECOC) and genetic algorithm (GA) which offsets this drawback. The proposed binarization transform novelty lies in the fact that the specific crossover, mutation and extension operators are defined by considering the theoretical properties of the ECOC framework in order to find an optimized coding matrix. The specification of these operations reduce the search space and speed up the convergence rate of GA. In addition, the proposed method is immune from being trapped in local minima. The quality of our method is evaluated using three well-known face databases: CMU PIE, Extended Yale B and FEI. The results demonstrate that the proposed binarization transform can result in a higher recognition accuracy without sacrificing the security of the protocol compared to alternative methods, such as binary discriminant analysis (BDA), discriminability-preserving (DP), chaos biohashing, and Discriminant-Genetic.