Eye detection using Gabor Filter and SVM

Eye detection has many applications in computer vision systems. A novel approach of eye detection for facial images using Gabor Filter and support vector machine (SVM) is proposed in this paper. Eye/non-eye patterns are rotated by different angels using Gabor Filter and then used to train SVM. In the proposed approach first face is extracted using skin colour information and later using Lab transform and Morphological operations eye pair candidates are detected which are given to SVM classifier to classify the detected eye pair candidates as eye or non-eye. The Lab and HSV colour space are used for face extraction and to find eye pair candidates. Separable Gabor filters are used to decrease computation time and the rotation-invariant characteristics of the Gabor Filter makes this method robust against rotation. The proposed approach is tested on rotated images of the GTAV[13] database and is also experimented on videos captured at VITS and the success rate achieved is 96%.