Robust face detection using Gabor filter features

In this paper, we present a classification-based face detection method using Gabor filter features. Taking advantage of the desirable characteristics of spatial locality and orientation selectivity of Gabor filters, we design four filters corresponding to four orientations for extracting facial features from local images in sliding windows. The feature vector based on Gabor filters is used as the input of the face/non-face classifier, which is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The effectiveness of the proposed method is demonstrated by experiments on a large number of images. We show that using both of the magnitude and phase of Gabor filter response as features, the detection performance is better than that using magnitude only, and using the real part only also performs fairly well. Our detection performance is competitive with those reported in the literature.