Full Text:   <7147>

Summary:  <1902>

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: 7003

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Qi-rong Mao

http://orcid.org/0000-0002-5021-9057

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

http://doi.org/10.1631/FITEE.1400209


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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Abstract: 
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.

基于Kinect的实时面部情感识别

目的:基于Kinect提取面部表情特征,实现对视频中面部情感实时识别。
创新点:基于Kinect提出一种新颖的面部情感识别方法,充分发挥出Kinect高速易用的优势,可实时对视频序列最新连续帧中表现出的情感进行综合判定。识别方法涉及两种不同形式的表情特征,对此我们也针对性地提出基于最大置信的融合算法。
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)。
结论:基于Kinect提取到的两种面部表情特征(面部运动单元信息和特征点坐标),提出一种新颖高效的面部情感识别方法,实现对视频中面部情感实时识别。

关键词:Kinect;情感识别;面部表情;实时分类;融合算法;支持向量机

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

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