Estimating Attention of Faces Due to its Growing Level of Emotions

R. Kumar, Jogendra Garain, D. Kisku, G. Sanyal
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引用次数: 3

Abstract

In the task of attending faces in the disciplined assembly (Like in examination hall or Silent public places), our gaze automatically goes towards those persons who exhibits their expression other than the normal expression. It happens due to finding of dissimilar expression among the gathering of normal. In order to modeling this concept in the intelligent vision of computer system, hardly some effective researches have been succeeded. Therefore, in this proposal we have tried to come out with a solution for handling such challenging task of computer vision. Actually, this problem is related to cognitive aspect of visual attention. In the literature of visual saliency authors have dealt with expressionless objects but it has not been addressed with object like face which exploits expressions. Visual saliency is a term which differentiates "appealing" visual substance from others, based on their feature differences. In this paper, in the set of multiple faces, 'Salient face' has been explored based on 'emotion deviation' from the normal. In the first phase of the experiment, face detection task has been accomplished using Viola Jones face detector. The concept of deep convolution neural network (CNN) has been applied for training and classification of different facial expression of emotions. Moreover, saliency score of every face of the input image have been computed by measuring their 'emotion score' which depends upon the deviation from the 'normal expression' scores. This proposed approach exhibits fairly good result which may give a new dimension to the researchers towards the modeling of an intelligent vision system which can be useful in the task of visual security and surveillance.
由于情绪水平的增长,估计面部的注意力
在有纪律的集会(如考场或安静的公共场所)中,我们的目光会自动转向那些表现出非正常表情的人。它的发生是由于在正常的集合中发现了不同的表达。为了在计算机智能视觉系统中对这一概念进行建模,目前还没有一些有效的研究取得成功。因此,在这个提案中,我们试图提出一个解决方案来处理这样一个具有挑战性的计算机视觉任务。实际上,这个问题与视觉注意的认知方面有关。在视觉显著性的文献中,作者已经处理了无表情的物体,但还没有处理像脸这样利用表情的物体。视觉显著性是一个术语,它区分“吸引人的”视觉物质与其他物质,基于它们的特征差异。本文在多面集合中,基于正常的“情绪偏差”对“显著面”进行了探索。在实验的第一阶段,使用Viola Jones人脸检测器完成了人脸检测任务。深度卷积神经网络(CNN)的概念已被应用于不同面部表情情绪的训练和分类。此外,通过测量输入图像的每个面部的“情绪得分”来计算显着性得分,这取决于与“正常表情”得分的偏差。该方法取得了较好的效果,为智能视觉系统的建模提供了一个新的方向,可用于视觉安全和监控任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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