An Across-Target Study on Visual Attentions in Facial Expression Recognition

As a simulation of human expression recognition, the studies on automatic expression recognition expect to draw useful enlightenment through close, accurate observation on human expression processing via advanced devices. Eye-trackers are mostly used devices that are technically designed to obtain eye-movement data. However, due to the discrepancy between target faces, across-target analysis is limited in these studies, and this much reduces the chance of finding the latent eye-behavior patterns. Through the utilization of correspondences between targets, this study achieves an across-target analysis to explore the attention pattern in expression recognition. The fixations from different targets are mapped onto a synthetic face to generate an across-target fixation map, and then tokenized with area of interests (AOI), measured in receiver operating characteristic (ROC) space, modeled by linear regression and compared through Pearson’s correlation. The resulted averaged correlation values vary in the range (0.60, 0.86), and illustrate that there is significant similarity between subjects when recognizing the same expression classes.