Full Text:   <8270>

Summary:  <2304>

CLC number: TP391.4

On-line Access: 2015-04-03

Received: 2014-06-12

Revision Accepted: 2014-10-22

Crosschecked: 2015-03-09

Cited: 15

Clicked: 9208

Citations:  Bibtex RefMan EndNote GB/T7714


Qi-rong Mao


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.4 P.272-282


Using Kinect for real-time emotion recognition via facial expressions

Author(s):  Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen

Affiliation(s):  Department of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China

Corresponding email(s):   mao_qr@ujs.edu.cn, pxyz@vip.qq.com

Key Words:  Kinect, Emotion recognition, Facial expression, Real-time classification, Fusion algorithm, Support vector machine (SVM)

Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen. Using Kinect for real-time emotion recognition via facial expressions[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(4): 272-282.

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A1 - Qi-rong Mao
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A1 - Yong-zhao Zhan
A1 - Xiang-jun Shen
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DOI - 10.1631/FITEE.1400209

emotion recognition via facial expressions (ERFE) has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units (AUs) and feature point positions (FPPs) tracked by kinect. A fusion algorithm based on improved emotional profiles (IEPs) and maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.

This paper proposes a method for facial expression recognition by combining 2D and 3D data which are captured by Kinect. The presented approach to FER (or ER) is rather obvious and straightforward, nevertheless it is valuable and worthy to be published.


方法:首先,运用Kinect中Face Tracking SDK从实时视频数据中追踪人脸、提取面部运动单元信息和特征点坐标(图3、4)。然后,将这两类特征信息并行处理,在它们各自特征通道中,特征数据经7元1-vs-1分类器组进行预识别,将得到的预识别结果存入缓存用于情感置信统计,置信度最高的即为此通道中的情感识别结果(图2)。最后,融合这两个特征通道的结果即可得到最终情感识别结果(图1)。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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