Abstract
Fast and robust face detection is necessary for many practical applications, especially for a high-performance face recognition system. However, most existing robust algorithms are computationally expensive. To reduce computational cost while retaining high detection accuracy and robustness, in this paper, we propose a new face detection method which combines a neural network (NN) and a genetic algorithm (GA). The NN serves as a face filter while the GA is used to search an image efficiently. The GA searches the image with a population of individuals, each representing a subwindow in the image. The subwindows are evaluated by how well they match the NN-based face filter. A face is indicated when the filter response of the best individual is above a given threshold. Experimental results show the effectiveness of the proposed method.
