MVAAM (multi-view active appearance model) optimized by multi-objective genetic algorithm

This paper presents an efficient algorithm of face alignment of multiple images by 2.5D Active Appearance Model (AAM). Currently with wide availability of inexpensive webcams a multi-view system is as practical as mono-view. To manage these multiple information obtained from multiview system we propose a new optimization technique of AAM. Our technique is based on Pareto multi-objective genetic optimization of NSGA-II (Non-dominated Sorting Genetic Algorithm). Our approach of multi-view AAM outperforms conventional single-view AAM (SVAAM) and is more accurate, robust and capable of performing face alignment with large lateral movements of a face. Sometimes face is oriented such that one of the camera of multi-view system do not hold a valid face information thus system has to discard the information from this camera and focus on to other camera. Our algorithm has this capability which makes it time efficient with respect to applying multiple instances of conventional SVAAM on multiple images. Algorithm is applied on number of multi-view images and compared with a system based on mono-view images. Results obtained validate our proposition.