Recognition of Facial Expressions Based on Information From the Areas of Highest Increase in Luminance Contrast

Author:

Babenko VitaliORCID,Alekseeva DariaORCID,Yavna DenisORCID,Denisova EkaterinaORCID,Kovsh EkaterinaORCID,Ermakov PavelORCID

Abstract

It is generally accepted that the use of the most informative areas of the input image significantly optimizes visual processing. Several authors agree that, the areas of spatial heterogeneity are the most interesting for the visual system and the degree of difference between those areas and their surroundings determine the saliency. The purpose of our study was to test the hy-pothesis that the most informative are the areas of the image of largest increase in total luminance contrast, and information from these areas is used in the process of categorization facial expressions. Using our own program that was developed to imitate the work of second-order visual mechanisms, we created stimuli from the initial photographic images of faces with 6 basic emotions and a neutral expression. These images consisted only of areas of highest increase in total luminance contrast. Initially, we determined the spatial frequency ranges in which the selected areas contain the most useful information for the recognition of each of the expressions. We then compared the expressions recognition accuracy in images of real faces and those synthe-sized from the areas of highest contrast increase. The obtained results indicate that the recognition of expressions in synthe-sized images is somewhat worse than in real ones (73% versus 83%). At the same time, the partial loss of information that oc-curs due to the replacing real and synthesized images does not disrupt the overall logic of the recognition. Possible ways to make up for the missing information in the synthesized images are suggested.

Publisher

FSFEI HE Don State Technical University

Subject

Cognitive Neuroscience,Experimental and Cognitive Psychology,Education

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3