Nonlocal contrast calculated by the second order visual mechanisms and its significance in identifying facial emotions

Abstract

Background: Previously obtained results indicate that faces are /preattentively/ detected in the visual scene very fast, and information on facial expression is rapidly extracted at the lower levels of the visual system. At the same time different facial attributes make different contributions in facial expression recognition. However, it is known, among the preattentive mechanisms there are none that would be selective for certain facial features, such as eyes or mouth. The aim of our study was to identify a candidate for the role of such a mechanism. Our assumption was that the most informative areas of the image are those characterized by spatial heterogeneity, particularly with nonlocal contrast changes. These areas may be identified /in the human visual system/ by the second-order visual /mechanisms/ filters selective to contrast modulations of brightness gradients. Methods: We developed a software program imitating the operation of these /mechanisms/ filters and finding areas of contrast heterogeneity in the image. Using this program, we extracted areas with maximum, minimum and medium contrast modulation amplitudes from the initial face images, then we used these to make three variants of one and the same face. The faces were demonstrated to the observers along with other objects synthesized the same way. The participants had to identify faces and define facial emotional expressions. Results: It was found that the greater is the contrast modulation amplitude of the areas shaping the face, the more precisely the emotion is identified. Conclusions: The results suggest that areas with a greater increase in nonlocal contrast are more informative in facial images, and the second-order visual /mechanisms/ filters can claim the role of /filters/ elements that detect areas of interest, attract visual attention and are windows through which subsequent levels of visual processing receive valuable information.