Eye detection based on head contour geometry and wavelet subband projection

We propose a novel two-step approach for eyes detection in complex scenes including both indoor and outdoor environments. This approach adopts face localization to eye extraction strategy. First, we use energy analysis to remove most noise-like regions to enhance face localization performance, and then use the head contour detection (HCD) approach to search for the best combinations of facial sides and head contours with an anthropometric measure, and thereafter the face-of-interest (FOI) region is located. In the meantime, via the deedging preprocessing for facial sides, a wavelet subband interorientation projection method is adopted to select eye-like candidates. Along with the geometric discrimination among the facial components, such as the eyes, nose, and mouth, this eye verification rule verifies the selected eyes candidates. The eye positions are then marked and refined by the bounding box of FOI region as the ellipse being the best fit of the facial oval shape. The experimental results demonstrate that the performance of our proposed method has significant improvement compared to others on three head-and-shoulder databases.