Robust face detection using local gradient patterns and evidence accumulation

This paper proposes a novel face detection method using local gradient patterns (LGP), in which each bit of the LGP is assigned the value one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients, and 0 otherwise. LGP representation is insensitive to global intensity variations like the other representations such as local binary patterns (LBP) and modified census transform (MCT), and to local intensity variations along the edge components. We show that LGP has a higher discriminant power than LBP in both the difference between face histogram and non-face histogram and the detection error based on the face/face distance and face/non-face distance. We also reduce the false positive detection error greatly by accumulating evidences from multi-scale detection results with negligible extra computation time. In experiments using the MIT+CMU and FDDB databases, the proposed LGP-based face detection followed by evidence accumulation method provides a face detection rate that is 5–27% better than those of existing methods, and reduces the number of false positives greatly.