Eye Detection Under Unconstrained Background by the Terrain Feature

Locating eyes in face images is an important step for automatic face analysis and recognition. In this paper, we present a novel approach for eye detection without finding the face region using topographic features. First, we argue that the eyes show certain topographic pattern if the gray-level face image is treated as a 3D terrain surface. Then a terrain map, which denotes the terrain type of each pixel, is derived from the original image by applying topographic classification approach. From the terrain map, eyes usually locate in the region around the pit-labeled pixels because of their intrinsic reflectance characteristic. At last, we construct Gaussian mixture model based probabilistic model to describe the distribution of pit-labeled candidates. The eye pair detection problem is transformed to maximize the probability that the selected candidate pair belongs to the eye space. Experiments show that our method has certain robustness to uncontrolled background