Machine learning for multi-view eye-pair detection.

While face and eye detection is well known research topics in the field of object detection, eye-pair detection has not been much researched. Finding the location and size of an eye-pair in an image containing a face can enable a face recognition application to extract features from a face corresponding to different entities. Furthermore, it allows us to align different faces, so that more accurate recognition results can be obtained. To the best of our knowledge, currently there is only one eye-pair detector, which is a part of the Viola–Jones object detection framework. However, as we will show in this paper, this eye-pair detector is not very accurate for detecting eye-pairs from different face images. Therefore, in this paper we describe several novel eye-pair detection methods based on different feature extraction methods and a support vector machine (SVM) to classify image patches as containing an eye-pair or not. To find the location of an eye-pair on unseen test images, a sliding window approach is used, and the location and size of the window giving the highest output of the SVM classifier are returned. We have tested the different methods on three different datasets: the IMM, the Caltech and the Indian face dataset. The results show that the linear restricted Boltzmann machine feature extraction technique and principal component analysis result in the best performances. The SVM with these feature extraction methods is able to very accurately detect eye-pairs. Furthermore, the results show that our best eye-pair detection methods perform much better than the Viola–Jones eye-pair detector.