Optimizing a Homomorphic Filter for Illumination Compensation In Face Recognition Using Population-Based Algorithms

Face recognition (FR) systems based on populationbased heuristics algorithms is common nowadays. Depends on the approach, there is a need for tuning parameters in preprocessing step using optimization algorithms. Regarding FR challenges, the illumination variation is one of the critical factors. The homomorphic filter (HF) is one of the methods that can compensate ination aiding to stabilize the face images obtained under different lighting conditions. However, the HF requires some optimal parameters which can be possibly determined through optimization process. Based on this context, in this paper, we attempt to investigate the application of two population-based algorithms, the Jaya and the Particle Swarm Optimization (PSO), in order to optimize the HF parameters in CMU-PIE and BIOINFO face databases. The high performance of both algorithms shows that they are suitable to compensate the illumination variation achieving 100% and 91.3% recognition rates on CMUPIE and BIO-INFO, respectively.