Facial expression recognition using shape signature feature

Asit Barman, P. Dutta
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引用次数: 10

Abstract

In this paper, we propose a novel framework for expression recognition by using salient landmarks induced shape signature. Detection of effective landmarks is achieved by appearance based models. A grid is formed using the landmark points and accordingly several triangles within the grid on the basis of a nose landmark reference point are formed. Normalized shape signature is derived from grid. Stability index is calculated from shape signature which is also exploited as significant feature to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are used to supplement the feature set. This enhanced feature set is fed into Multilayer Perceptron (MLP) and Nonlinear AutoRegressive with eXogenous (NARX) to differentiate the expressions into different categories. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG benchmark databases to conduct and validate our experiment and established its performance superiority over other existing competitors.
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基于形状特征的面部表情识别
本文提出了一种基于显著标志诱导形状特征的表情识别框架。有效地标的检测是通过基于外观的模型实现的。使用地标点形成网格,并相应地在网格内以鼻子地标参考点为基础形成若干三角形。归一化形状特征是由网格导出的。从形状特征中计算稳定性指数,并利用形状特征作为识别面部表情的重要特征。使用范围、矩、偏度、峰度和熵等统计度量来补充特征集。该增强的特征集被输入到多层感知器(MLP)和非线性自回归外生(NARX)中,以区分不同类别的表达。我们在Cohn-Kanade (CK+), JAFFE, MMI和MUG基准数据库上对我们提出的系统进行了研究,并验证了我们的实验,并确定了其优于其他现有竞争对手的性能优势。
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