Facial expression recognition based on local region specific features and support vector machines
Facial expression recognition based on local region specific features and support vector machines
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DOI：
10.1007/s11042-016-3418-y
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发表时间：
2017-03-01
影响因子：
3.6
通讯作者：
Park, San Hyun
中科院分区：
文献类型：
--
作者：
Ghimire, Deepak;Jeong, Sunghwan;Park, San Hyun
面部表情是人类表达情感和意图的最有力、最自然、最直接的手段之一。人脸表情识别在人机交互、认知科学、人类情感分析、个性发展等方面有着广泛的应用。本文提出了一种基于支持向量机分类器的人脸表情识别方法。通常，用于识别面部表情的外观特征通过将面部区域划分为规则网格（整体表示）来计算。但是，在本文中，我们提取区域特定的外观特征，通过将整个人脸区域划分为特定领域的局部区域。还从对应的域特定区域提取几何特征。此外，采用增量搜索的方法确定重要的局部区域，降低了特征维数，提高了识别精度。使用域特定区域的特征的面部表情识别的结果也与使用整体表示所获得的结果进行了比较。所提出的面部表情识别系统的性能已在公开的扩展Cohn-Kanade（CK+）面部表情数据集上进行了验证。
Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.