Facial expression recognition using entropy and brightness features

Rizwan Ahmed Khan, Alexandre Meyer, H. Konik, S. Bouakaz
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引用次数: 8

Abstract

This paper proposes a novel framework for universal facial expression recognition. The framework is based on two sets of features extracted from the face image: entropy and brightness. First, saliency maps are obtained by state-of-the-art saliency detection algorithm i.e. “frequency-tuned salient region detection”. Then only localized salient facial regions from saliency maps are processed to extract entropy and brightness features. To validate the performance of saliency detection algorithm against human visual system, we have performed a visual experiment. Eye movements of 15 subjects were recorded with an eye-tracker in free viewing conditions as they watch a collection of 54 videos selected from Cohn-Kanade facial expression database. Results of the visual experiment provided the evidence that obtained saliency maps conforms well with human fixations data. Finally, evidence of the proposed framework's performance is exhibited through satisfactory classification results on Cohn-Kanade database.
基于熵和亮度特征的面部表情识别
提出了一种新的通用面部表情识别框架。该框架基于从人脸图像中提取的两组特征:熵和亮度。首先,通过最先进的显著性检测算法即“频率调谐显著区域检测”获得显著性图。然后对显著性图中局部显著性区域进行处理,提取熵和亮度特征。为了验证显著性检测算法在人类视觉系统下的性能,我们进行了视觉实验。在自由观看的条件下,15名受试者在观看从科恩-卡纳德面部表情数据库中挑选出来的54段视频时,用眼动仪记录了他们的眼球运动。视觉实验结果表明,所获得的显著性图与人眼注视数据吻合较好。最后,通过Cohn-Kanade数据库上令人满意的分类结果展示了所提出框架的性能证据。
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