Face Detection Using the 3×3 Block Rank Patterns of Gradient Magnitude Images

Face detection locates faces prior to various face-related applications. The objective of face detection is to determine whether or not there are any faces in an image and, if any, the location of each face is detected. Face detection in real images is challenging due to large variability of illumination and face appearances. This paper proposes a face detection algorithm using the 3×3 block rank patterns of gradient magnitude images and a geometrical face model. First, the illumination-corrected image of the face region is obtained using the brightness plane that is produced using the locally minimum brightness of each block. Next, the illumination-corrected image is histogram equalized, the face region is divided into nine (3×3) blocks, and two directional (horizontal and vertical) gradient magnitude images are computed, from which the 3×3 block rank patterns are obtained. For face detection, using the FERET and GT databases three types of the 3×3 block rank patterns are a priori determined as templates based on the distribution of the sum of the gradient magnitudes of each block in the face candidate region that is also composed of nine (3×3) blocks. The 3×3 block rank patterns roughly classify whether the detected face candidate region contains a face or not. Finally, facial features are detected and used to validate the face model. The face candidate is validated as a face if it is matched with the geometrical face model. The proposed algorithm is tested on the Caltech database images and real images. Experimental results with a number of test images show the effectiveness of the proposed algorithm.