Learning-by-synthesis for accurate eye detection

Cascade regression framework has been successfully applied to facial landmark detection and achieves state-of-the-art performance recently. It requires large number of facial images with labeled landmarks for training regression models. We propose to use cascade regression framework to detect eye center by capturing its contextual and shape information of other related eye landmarks. While for eye detection, it is time-consuming to collect large scale training data and it also can be unreliable for accurate manual annotation of eye related landmarks. In addition, it is difficult to collect enough training data to cover various illuminations, subjects with different head poses and gaze directions. To tackle this problem, we propose to learn cascade regression models from synthetic photorealistic data. In our proposed approach, eye region is coarsely localized by a facial landmark detection method first. Then we learn the cascade regression models iteratively to predict the eye shape updates based on local appearance and shape features. Experimental results on benchmark databases such as BioID and GI4E show that our proposed cascade regression models learned from synthetic data can accurately localize the eye center. Comparisons with existing methods also demonstrates our proposed framework can achieve preferable performance against state-of-the-art methods.