HMOAM: hybrid multi-objective genetic optimization for facial analysis by appearance model

Successful hybridization of single-objective evolutionary algorithm with gradient based methods has shown promising results. However, studies of hybridized Multi-Objective Evolutionary Algorithm are limited, especially in the domain of image analysis. This paper presents a novel methodology of hybridization of multi-objective genetic algorithm for the real world optimization problem of facial analysis of multiple camera images by 2.5D Appearance Model. Facial large lateral movements make acquisition and analysis of facial images by single camera inefficient. Moreover, non-convex multi-dimensional search space formed by the face search by appearance model requires an efficient optimization methodology. Currently, with wide availability of inexpensive cameras, a multi-view system is as practical as a single-view system. To manage these multiple informations, multi-objective genetic algorithm is employed to optimize the face search. To efficiently tackle the problem of non-convexity of the search space, hybridization of NSGA-II (Non-dominated Sorting Genetic Algorithm) with Gradient Descent is proposed in this paper. For this hybridization, we propose a gradient operator in NSGA-II, which computes gradients of the solutions in conjunction with the existing operator of mutation. Thus, it does not increase the computational cost of the system. Another proposition includes a unique method of calculating the relevant information of each camera in a multiple camera system which makes the hybridization procedure efficient and robust. Our proposed algorithm is applied on different facial poses of CMU-PIE database, webcam face images and synthetic face images, and the results are compared with a single view system and a non-hybrid multiple camera system. Simulation results validate the efficiency, accuracy and robustness achieved.