Robust eye detection using deeply-learned gaze shifting path

Eye detection is a very useful technique in many intelligent applications. Since the importance of eyes to human beings, eye detection technique is an indispensable component in intelligent systems, e.g., emotional analysis, iris detection and gaze estimation. Recently, there have been proposed a large number of methods for eye detection, wherein good performances have been achieved. But these methods cannot take human visual perception into account, that is to say, human beings will first pay attention to the eyes when they are communicating with each other, and then nose, then mouth. In addition, their geometric positions are almost fixed, i.e., eyes are above the nose and mouth, and eyes are on both sides of the nose. So in our work, a novel method for eye detection is proposed using human visual perception. More specifically, we first derive object patches from a large quantity of training images. Then, a geometry-preserved object patches ranking method is designed to effectively mimic human visual mechanism when human beings are communicating with each other. After that, these ordered object patches will be fed into CNN to extract patch-level deep features, then patch-level deep features will be represented by deep representations. Finally, eye detection can be achieved using learned deep representation. Experimental results on different database show that our method can achieve high efficiency and accuracy of eye detection.