Real-time face detection using circular sliding of the Gabor energy and neural networks

Face detection is one of the most important subjects in image processing. Over time, researchers have paid much attention to the subject, and they have made tremendous progress in the quality of face detection. In addition to the quality of face detection, the speed of face detection is of prime importance. In this paper, a real-time approach is presented for face detection using the Gabor filters and the neural networks that can be implemented in IoT devices. The Gabor filters are one of the most powerful tools in image processing, but they are rarely used in real-time applications due to high computational complexity. To overcome the problem, a new algorithm is proposed for processing images and detecting faces called circular sliding window (CSW). This new algorithm can reduce the number of sub-images generated by almost 98% related to the sliding window algorithm, in frontal face images which have symmetry. Also, a new Gabor feature called compressed Gabor feature (CGF) is employed which improves the speed of classification due to reducing the size of feature vector of the neural network. In the proposed method, the best speed of face detection and the worst speed of face detection for faces with size of 64 x 64 pixels are 0.0072 and 0.0092 s, respectively. The sensitivity of face detection in the proposed method is 95%, approximately.