Person-independent eye gaze prediction from eye images using patch-based features

This paper delivers a preliminary attempt towards person-independent appearance-based gaze estimation. Conventional methods need to assume training and test data collected from the same person, otherwise eye shape difference due to individuality will affect the estimation severely. To solve this problem, the key idea in this paper is to extract from eye images more advanced eye features, which helps learn a person-independent relationship between eye gaze change and eye appearance variation. To this end, we propose employing the advantages of recent sparse auto-encoding techniques. We partition any eye image into small patches which can overlap with each other. With patches from many images, we learn a codebook comprising a set of bases, which can reconstruct any eye image patch with sparse coefficients. By examining these coefficients, we can analyze the eye shape more effectively. Finally, we produce the eye features by pooling the coefficients at different scales, and then combine these subfeatures from different codebooks. Experimental results show that the proposed method achieves good accuracy on a public dataset and it also outperforms conventional methods by a large margin.