Face reconstruction using fixation positions and foveated imaging

Face representation is important for face recognition system. Though most popular face representations are based on uniform grid sampling, some recent face recognition systems adopt weighted sampling on the different regions of a face. Psychological analysis of visual attention or human eye fixations on human face images may suggest some cues for face representation. human visual system (HVS) gives different weight to different region of human face via space-variant sampling on fovea and non-uniform distribution of fixations. This paper focuses on the problem of simulation of the foveated imaging phenomenon in HVS, and introduction of foveated imaging method into reconstruction of face in region of interest (ROI) using different fixation sources. We compare the effectiveness of actual fixation on reconstruction of face in ROI with uniform, random distribution fixation as well as fixation generated by artificial model. The experimental results on 100 face images from FRGC [7] data set show that actual fixation positions and model-generated fixation positions reconstruct the face in ROI with considerably better quality. A further analysis on the statistics of fixation positions also shows that the distribution of the fixation points is consistent with the weights of different regions on face images used in some other face recognition systems.