[1]张御宇,倪蓉蓉,杨 彪.基于改进随机森林分类器在RGBD面部表情上的应用研究[J].南京师范大学学报(自然科学版),2019,42(01):82.[doi:10.3969/j.issn.1001-4616.2019.01.013]
　Zhang Yuyu,Ni Rongrong,Yang Biao.Research on Bimodal Facial Expression Based onImproved Random Forest Classifier[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(01):82.[doi:10.3969/j.issn.1001-4616.2019.01.013]
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基于改进随机森林分类器在RGBD面部表情上的应用研究()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年01期
页码:
82
栏目:
·人工智能算法与应用专栏·
出版日期:
2019-03-20

文章信息/Info

Title:
Research on Bimodal Facial Expression Based onImproved Random Forest Classifier
文章编号:
1001-4616(2019)01-0082-08
作者:
张御宇1; 倪蓉蓉2; 杨 彪1
(1.常州大学信息科学与工程学院,江苏 常州 213164)(2.常州纺织服装职业技术学院,江苏 常州 213164)
Author(s):
Zhang Yuyu1; Ni Rongrong2; Yang Biao1
(1.School of Information Science and Engineering,Changzhou University,Changzhou 213164,China)(2.Changzhou Vocational Institute of Textile and Garment,Changzhou 213164,China)
关键词:
面部检测; 面部表情识别; RGBD面部图像; 特征选择随机森林; 面部几何特征
Keywords:
face detection; facial expression recognition; RGBD facial images; feature selection based random forest; facial geometrical feature
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.01.013
文献标志码:
A
摘要:
面部表情识别是机器感知人类情绪变化的重要途径. 利用面部RGB图像可以进行表情识别,但是容易受到光照变化影响,而且较难刻画细微表情变化. 对采用RGBD面部图像识别6种基本面部表情(高兴、悲伤、愤怒、沮丧、恐惧以及惊讶)进行研究. 首先利用深度图像鲁棒地检测面部; 然后在面部灰度图像中检测并跟踪二维面部标记点,并添加对应的深度信息构造深度面部几何特征,从而有效识别细微表情变化; 最后利用基于特征选择的随机森林分类器对不同面部表情进行识别. 基准数据库上的对比实验结果表明本文算法的表情识别准确率高于主流基于手动提取特征的面部表情识别方法,接近基于卷积神经网络的识别算法性能.
Abstract:
Facial expression recognition is an important way for machines to understand expression changes of human beings. Expression recognition using RGB facial images suffers from illumination changes and detail depicting in minor emotion changes. This work employ RGBD facial images to recognize 6 basic facial expressions(happiness,sadness,angry,disgust,fear and surprise). Depth images are used for robust face detection initially. Gray-scale facial images are employed to detect and to track 2D facial landmark points from the detected face region. Then,corresponding depth information is added into these points to construct depth facial geometrical feature which can recognize minor expression changes more effectively. Finally,a random forest classifier based on feature selection is designed to recognize different facial expressions. Results of comparative evaluations on benchmarking datasets verify the fact that our approach outperforms several state-of-the-art facial expression approaches,which use hand-crafted features,in recognizing six basic facial expressions. Meanwhile,our approach achieves almost similar performance comparing with the convolutional neural network based expression recognition algorithms.

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备注/Memo

备注/Memo:
收稿日期:2018-08-16.
基金项目:国家自然科学基金(61501060)、江苏省科技厅青年基金(BK20150271)、江苏省道路载运工具新技术应用重点实验室开放课题(ZMF15020068).
通讯联系人:杨彪,博士,讲师,研究方向:机器视觉、模式识别. E-mail:yb6864171@cczu.edu.cn
更新日期/Last Update: 2019-03-30