Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition

被引:17
|
作者
Ghimire, Deepak [1 ]
Lee, Joonwhoan [1 ]
机构
[1] Chonbuk Natl Univ, Dept Comp Engn, Jeonju 561756, South Korea
来源
基金
新加坡国家研究基金会;
关键词
Bagging; Ensemble Learning; Extreme Learning Machine; Facial Expression Recognition; Histogram of Orientation Gradient;
D O I
10.3745/JIPS.02.0004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.
引用
收藏
页码:443 / 458
页数:16
相关论文
共 50 条
  • [41] Genetic ensemble of extreme learning machine
    Xue, Xiaowei
    Yao, Min
    Wu, Zhaohui
    Yang, Jianhua
    [J]. NEUROCOMPUTING, 2014, 129 : 175 - 184
  • [42] A survival ensemble of extreme learning machine
    Hong Wang
    Jianxin Wang
    Lifeng Zhou
    [J]. Applied Intelligence, 2018, 48 : 1846 - 1858
  • [43] Comparing ensemble strategies for deep learning: An application to facial expression recognition
    Renda, Alessandro
    Barsacchi, Marco
    Bechini, Alessio
    Marcelloni, Francesco
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2019, 136 : 1 - 11
  • [44] Recognition and intensity estimation of Facial expression using ensemble classifiers
    Nomiya H.
    Sakaue S.
    Hochin T.
    [J]. International Journal of Networked and Distributed Computing, 2016, 4 (4) : 203 - 211
  • [45] Ensemble based Constrained-Optimization Extreme Learning Machine for Landmark Recognition
    Zhao, Yanfei
    Cao, Jiuwen
    Lai, Xiaoping
    Yin, Chun
    Chen, Tao
    [J]. 2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 3884 - 3889
  • [46] Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals
    Peng, Fulai
    Chen, Cai
    Lv, Danyang
    Zhang, Ningling
    Wang, Xingwei
    Zhang, Xikun
    Wang, Zhiyong
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2022, 16
  • [47] Facial Expression Recognition using Transfer Learning
    Ramalingam, Soodamani
    Garzia, Fabio
    [J]. 2018 52ND ANNUAL IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST), 2018, : 152 - 156
  • [48] Facial Expression Recognition Using Deep Learning
    Shehu, Harisu Abdullahi
    Sharif, Md Haidar
    Uyaver, Sahin
    [J]. FOURTH INTERNATIONAL CONFERENCE OF MATHEMATICAL SCIENCES (ICMS 2020), 2021, 2334
  • [49] Facial Expression Recognition Using Supervised Learning
    Suneeta, V. B.
    Purushottam, P.
    Prashantkumar, K.
    Sachin, S.
    Supreet, M.
    [J]. COMPUTATIONAL VISION AND BIO-INSPIRED COMPUTING, 2020, 1108 : 275 - 285
  • [50] Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization-extreme learning machine
    Zhou, Zhiyu
    Xu, Rui
    Wu, Dichong
    Zhu, Zefei
    Wang, Haiyan
    [J]. OPTICAL ENGINEERING, 2016, 55 (09)