Effective discretization of Gabor features for real-time face detection

We describe a real-time face detector based on Gabor features. While Gabor features often lead to improved performance, they are often avoided as they are perceived as being computationally expensive. We address this in two ways. First, we propose an efficient discrete encoding method for the Gabor feature vector. This enables us to use a computationally efficient multi-stage classifier based on boosting and winnowing. Second, we accelerate computationally complex computations using the parallelization provided by graphics processing units (GPUs). With these innovations, the resulting detector runs at 16.8 fps for 640 x 480 images on a PC equipped with an i5 CPU and a GTX 465 graphic card.