Using Particle Swarm Optimization for scaling and rotation invariant face detection
Ermioni Marami, Anastasios Tefas · 2010
Common face detection algorithms exhaustively search in all possible locations in the image for precisely located, frontal faces. In this paper, a novel face detection algorithm based on Particle Swarm Optimization (PSO) method for searching in the image is proposed. The algorithm uses a linear Support Vector Machine (SVM) as fast and accurate classifier and searches for a face in four dimensions: plane, orientation of the face, size of the face. Using PSO, the exhaustive search in all possible combinations of the 4D coordinates can be avoided, saving time and decreasing the computational complexity. Moreover, linear SVMs are proved to be a powerful and fast classifier for demanding applications. Experimental results under real recording conditions in the BioID and VALID database are very promising and indicate the potential use of the proposed approach to real applications.