Abstract
Video-sequences appear sharpness even blurred images inserted into them. This effect, called sharpness constancy (Ramachandran et al, 1974), has been demonstrated empirically. Video-sequence images are actually somewhat blurred, but they look sharp when the video is played. We have examined effects of motion information of a face on the recognition by the method of varying the degree of blur among facial expressions.
Methods: We prepared video-sequences of 2 facial expressions (happy and sad), each of which comprised 26 frames. A video-sequence of 29.97 fps was mixed alternately the half of the 26 frames was blurred by Gaussian filtering with the non-blurred frame. Video-sequences were set by 3 scales (Gaussian filter radius 0, 4, 8 pixel). Also, Blurred still images were set by ten scales (Gaussian filter radius 0–9 pixel) as comparison stimulus. 2 observers judged the perceived sharpness of the movies by comparing the movie to a blurred still image.
Results: In case that the value of blur of video-sequences was large (4 or 8 pixel), observers judged the value of blur of still image lower than the value of blur of video-sequences. However, appearance keeps sharpness when video-sequences of sad faces show than when that of happy faces show.
Conclusions: We found that sharpness constancy occurs in video-sequences of facial expression, and this effect differs among facial expressions. These results suggest that a motion detecting mechanism on the recognition of facial expression depends on the spatial frequency component of facial expression. Two explanation of this effect are possible: 1. Motion information of a face reconstructs the high spatial frequency information. The reconstruction ratio on happy faces is larger than that on sad faces. 2. Blurred images of a video-sequence were neglected. We can easy recognize the happy face with low spatial frequency, but we are hard to recognize sad faces with low spatial frequency.
This work was supported by MEXT grant (16330144).