The 3D Emotion Recognition Using SVM and HoG Features

被引：0

作者：

Savakar, Dayanand G. ^{[1
]}

Hosur, Ravi ^{[2
]}

机构：

[1] Rani Channamma Univ, Dr PG Halakatti Post Grad Ctr Vachana Sangama, Dept Comp Sci, Vijayapur 586108, Karnataka, India

[2] BLDEAs VP Dr PG Halakatti Coll Engn & Technol, Dept Comp Sci & Engn, Vijayapur 586103, Karnataka, India

来源：

INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS | 2020年 / 20卷 / 03期

关键词：

Emotion; features; classification; 3D face; expression recognition; FACIAL EXPRESSION RECOGNITION;

D O I：

10.1142/S0219467820500199

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Emotion recognition is becoming commercially popular due to the major role of analytics in various aspects of marketing and strategy management. Several papers have been proposed in emotion recognition. They are mainly classified in the past under 2D and 3D emotion recognition, out of which 2D emotion recognition has been more popular. Various aspects like facial posture, light intensity variations and sensor-independent recognition have been studied by different authors in the past. However, in reality, 3D emotion recognition has been found to be more efficient which has a broader area of use. In this paper, a 3D tracking plane with 2D feature points has enabled us to recognize emotions by statistical voting method from all planes having over threshold number of points in their respective contour area. The proposed technique's results are comparable to existing methods in terms of time, space complexity and accuracy improvement.

引用

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