Fusion of Features for the Effective Facial Expression Recognition

被引：0
|
作者：
Kumari, Jyoti [1 ]
Rajesh, R. [1 ]
Kumar, Abhinav [1 ]
机构：
[1] Cent Univ South Bihar, Dept Comp Sci, Patna, Bihar, India
来源：
2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1 | 2016年
关键词：
Facial Expression Recognition (FER); Histogram of Oriented Gradients (HOG); Local Directional Pattern (LDP); Local Binary Pattern (LBP);
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Facial Expression is the easy way of telling/showing inner feelings. The Facial Expression Recognition system has many applications including human behavior understanding, detection of mental disorders, synthetic human expressions and many more. This paper presents a quick survey of facial expression recognition as well as a comparative study of various features on JAFFE and CK datasets. It mainly focuses on appearance based techniques. Recently, HOG has been widely used for feature extraction in image. It is found in our experiment that HOG feature gives comparable good recognition rate in facial expression recognition. Fusion of LBP with LGC and Fusion of HOG with other features like LDP and wavelets also improved their respective recognition rates.
引用
收藏
页码：457 / 461
页数：5
相关论文
共 50 条
  • [31] Facial expression recognition using iterative fusion of MO-HOG and deep features
    Hao Wang
    Senbing Wei
    Baofu Fang
    The Journal of Supercomputing, 2020, 76 : 3211 - 3221
  • [32] Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features
    Viswanatha Reddy, G.
    Dharma Savarni, C.V.R.
    Mukherjee, Snehasis
    Cognitive Systems Research, 2020, 62 : 23 - 34
  • [33] Facial expression recognition based on hybrid features and multiple HMMs fusion for image sequences
    School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao, 2008, 7 (900-905):
  • [34] Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences
    Wang Xiaohua
    Xia Chen
    Hu Min
    Ren Fuji
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2018, 40 (03) : 626 - 632
  • [35] Multiple classifiers fusion for facial expression recognition
    Chuanjie Zhang
    Changming Zhu
    Granular Computing, 2022, 7 : 171 - 181
  • [36] Facial expression recognition based on data fusion
    Zhang, Youmei
    Zhang, Wei
    Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition), 2016, 48 (06): : 160 - 164
  • [37] Fusion of Feature Sets for Facial Expression Recognition
    Navran, Mina
    Charkari, Nasrollah Moghadam
    2014 7TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), 2014, : 507 - 512
  • [38] EFFICIENT FACIAL AND FACIAL EXPRESSION RECOGNITION USING CANONICAL CORRELATION ANALYSIS FOR TRANSFORM DOMAIN FEATURES FUSION AND CLASSIFICATION
    El-Shazly, Ehab H.
    Abdelwahab, Moataz M.
    Taniguchi, Rin-ichiro
    2015 11TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY & INTERNET-BASED SYSTEMS (SITIS), 2015, : 639 - 644
  • [39] Facial expression recognition Based on Feature Fusion
    Jian, Chen
    FIFTH INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION, 2020, 11526
  • [40] Multiple classifiers fusion for facial expression recognition
    Zhang, Chuanjie
    Zhu, Changming
    GRANULAR COMPUTING, 2022, 7 (01) : 171 - 181
12345