Facial Expression Recognition Using Face-Regions

被引:0
|
作者
Lekdioui, Khadija [1 ,2 ]
Ruichek, Yassine [1 ]
Messoussi, Rochdi [2 ]
Chaabi, Youness [2 ]
Touahni, Raja [2 ]
机构
[1] Univ Bourgogne Franche Comte, Arts & Metiers, CNRS, Le2i FRE2005, F-90010 Belfort, France
[2] Univ Ibn Tofail, Lab Syst Telecommun & Ingn Decis LASTID, BP 133, Kenitra 14000, France
来源
2017 3RD INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP) | 2017年
关键词
Facial expression recognition; facial landmarks; Facial decomposition; feature descriptor; SVM;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a facial expression recognition method based on a novel facial decomposition. First, seven regions of interest (ROI), representing the main components of face (left eyebrow, right eyebrow, left eye, right eye, between eyebrows, nose and mouth), are extracted using facial landmarks detected by IntraFace algorithm. Then, different local descriptors, such as LBP, CLBP, LTP and Dynamic LTP, are used to extract features. Finally, feature vector, representing face image, is fed into a multiclass support vector machine to achieve the recognition task. Experimental results on two public datasets show that the proposed method outperforms state of the art methods based on other facial decompositions.
引用
收藏
页码:295 / 300
页数:6
相关论文
共 50 条
  • [21] Facial Expression Recognition Using Eigenspaces
    Chakrabarti, Debasmita
    Dutta, Debtanu
    FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE: MODELING TECHNIQUES AND APPLICATIONS (CIMTA) 2013, 2013, 10 : 755 - 761
  • [22] Facial expression recognition based upon human cognitive regions
    Zhang, Huiquan
    Luo, Sha
    Yoshie, Osamu
    IEEJ Transactions on Electronics, Information and Systems, 2014, 134 (08) : 1148 - 1156
  • [23] Facial Expression Recognition Using AAMICPF
    Lee, Jun-Sung
    Oh, Chi-Min
    Lee, Chil-Woo
    HUMAN-COMPUTER INTERACTION: INTERACTION TECHNIQUES AND ENVIRONMENTS, PT II, 2011, 6762 : 268 - 274
  • [24] MiniExpNet: A small and effective facial expression recognition network based on facial local regions
    Jin, Xing
    Jin, Zhong
    NEUROCOMPUTING, 2021, 462 : 353 - 364
  • [25] Facial expression recognition using digital signature feature descriptor
    Kiran Talele
    Kushal Tuckley
    Signal, Image and Video Processing, 2020, 14 : 701 - 709
  • [26] Facial expression recognition using digital signature feature descriptor
    Talele, Kiran
    Tuckley, Kushal
    SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (04) : 701 - 709
  • [27] Automatic Facial Expression Recognition Using Combined Geometric Features
    Sharma, Garima
    Singh, Latika
    Gautam, Sumanlata
    3D RESEARCH, 2019, 10 (02)
  • [28] An Improved Method for Facial Expression Recognition using Hybrid Approach of CLBP and Gabor Filter
    Sharma, Sakshi
    Verma, Akhilesh
    Tyagi, Divya
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2017, : 1019 - 1024
  • [29] Facial Expression Recognition Using Facial Expression Intensity Characteristics of Thermal Image
    Yoshitomi, Yasunari
    Asada, Taro
    Kato, Ryota
    Tabuse, Masayoshi
    JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE, 2015, 2 (01): : 5 - 8
  • [30] ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition
    Rawal, Niyati
    Koert, Dorothea
    Turan, Cigdem
    Kersting, Kristian
    Peters, Jan
    Stock-Homburg, Ruth
    FRONTIERS IN ROBOTICS AND AI, 2022, 8