Multi-cues eye detection on gray intensity image
This paper presents a novel eye detection method for gray intensity image. The precise eye position can be located if the eye windows are accurately detected. The proposed method uses multi-cues for detecting eye windows from a face image. Three cues from the face image are used. Each cue indicates the positions of the potential eye windows. The first cue is the face intensity because the intensity of eye regions is relatively low. The second cue is based on the estimated direction of the line joining the centers of the eyes. The third cue is from the response of convolving the proposed eye variance filter with the face image. Based on the three cues, a cross-validation process is performed. This process generates a list of possible eye window pairs. For each possible case, variance projection function is used for eye detection and verification. A face database from MIT AI laboratory, which contains 930 face images with different orientations and hairstyles captured from different people, is used to evaluate the proposed system. The detection accuracy is 92.5%.
eye detection eye variance filter face recognition face detection