Integrating Geometric and Textural Features for Facial Emotion Classification Using SVM Frameworks

被引:10
作者
Datta, Samyak [1 ]
Sen, Debashis [2 ]
Balasubramanian, R. [1 ]
机构
[1] Indian Inst Technol, Dept Comp Sci & Engn, Roorkee, Uttar Pradesh, India
[2] Indian Inst Technol, Dept Elect & Elect Commun Engn, Kharagpur, W Bengal, India
来源
PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2016, VOL 1 | 2017年 / 459卷
关键词
Emotion classification; Geometric features; Textural features; Local binary patterns; DAGSVMs; RECOGNITION;
D O I
10.1007/978-981-10-2104-6_55
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present a fast facial emotion classification system that relies on the concatenation of geometric and texture-based features. For classification, we propose to leverage the binary classification capabilities of a support vector machine classifier to a hierarchical graph-based architecture that allows multi-class classification. We evaluate our classification results by calculating the emotion-wise classification accuracies and execution time of the hierarchical SVM classifier. A comparison between the overall accuracies of geometric, texture-based, and concatenated features clearly indicates the performance enhancement achieved with concatenated features. Our experiments also demonstrate the effectiveness of our approach for developing efficient and robust real-time facial expression recognition frameworks.
引用
收藏
页码:619 / 628
页数:10
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