Robust Eye Gaze Estimation

Eye gaze detection under challenging lighting conditions is a non-trivial task. Pixel intensity and the shades around the eye region may change depending on the time of day, location, or due to artificial lighting. This paper introduces a lighting-adaptive solution for robust eye gaze detection. First, we propose a binarization and cropping technique to limit our region of interest. Then we develop a gradient-based method for eye-pupil detection; and finally, we introduce an adaptive eye-corner detection technique that altogether lead to robust eye gaze estimation. Experimental results show the outperformance of the proposed method compared with related techniques

Photo collage-based photograph display system on mobile computing platform

In the last few decades, mobile computing platform technology has grown rapidly, as observed from smart phones that have quickly become ubiquitous. The mobile computing platform is the most widely used platform in our life today, and digital photographs captured through these devices have become routine for most people. In this study, we propose a novel artistic method for displaying photographs in mobile devices as a photo collage. Using our system, users can view a representative photograph as a collage of photographs associated with a certain event and access each of photographs individually. To implement this, we employ centroidal Voronoi diagram to obtain an even distribution of tiles, and use the sites as the location of tiles. We use the edge avoidance technique to prevent tiles from being located across the edges. To obtain the direction of tiles that follow near a strong edge, we employ the Edge tangent Flow field and use the field as the directions of tiles. Finally, we search for photographs that best match the tiles calculated above by using a thumbnail difference metric