Facial expression recognition and analysis: A comparison study of feature descriptors

被引：0
作者：
Liew, Chun Fui [1 ]
Yairi, Takehisa [1 ]
机构：
[1] University of Tokyo, Bunkyo, Tokyo
来源：
IPSJ Transactions on Computer Vision and Applications | 2015年 / 7卷
基金：
日本学术振兴会;
关键词：
Binary Robust Independent Elementary Features; Facial expression recognition; Gabor filter; Haar filter; Histogram of oriented gradients; Local binary patterns;
D O I：
10.2197/ipsjtcva.7.104
中图分类号：
学科分类号：
摘要：
Facial expression recognition (FER) is a crucial technology and a challenging task for human-computer interaction. Previous methods have been using different feature descriptors for FER and there is a lack of comparison study. In this paper, we aim to identify the best features descriptor for FER by empirically evaluating five feature descriptors, namely Gabor, Haar, Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and Binary Robust Independent Elementary Features (BRIEF) descriptors. We examine each feature descriptor by considering six classification methods, such as k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Adaptive Boosting (AdaBoost) with four unique facial expression datasets. In addition to test accuracies, we present confusion matrices of FER. We also analyze the effect of combined features and image resolutions on FER performance. Our study indicates that HOG descriptor works the best for FER when image resolution of a detected face is higher than 48×48 pixels. © 2015 Information Processing Society of Japan.
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收藏
页码：104 / 120
页数：16
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共 67 条
  • [1] Gabor D., Theory of communication, Journal of the Institution of Electrical Engineers, 93, 26, pp. 429-457, (1946)
  • [2] Papageorgiou C., Oren M., Poggio T., A general framework for object detection, Proc. 6th International Conference on Computer Vision, pp. 555-562, (1998)
  • [3] Ojala T., Pietikainen M., Harwood D., Performance evaluation of texture measures with classification based on kullback discrimination of distributions, Proc. 12th International Conference on Pattern Recognition, pp. 582-585, (1994)
  • [4] Dalal N., Triggs B., Histograms of oriented gradients for human detection, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, (2005)
  • [5] Ozuysal M., Calonder M., Lepetit V., Fua P., Fast keypoint recognition using random ferns, IEEE Trans. Pattern Analysis and Machine Intelligence, 32, 3, pp. 448-461, (2010)
  • [6] Liew C.F., Yairi T., A comparison study of feature spaces and classification methods in facial expression recognition, Proc. IEEE International Conference on Robotics and Biomimetics, pp. 1294-1299, (2013)
  • [7] Rudovic O., Patras I., Pantic M., Regression-based multi-view facial expression recognition, Proc. 20th International Conference on Pattern Recognition, pp. 4121-4124, (2010)
  • [8] Pantic M., Rothkrantz L., Expert system for automatic analysis of facial expressions, Journal of Image and Vision Computing, 18, 11, pp. 881-905, (2000)
  • [9] Asthana A., Saragih J., Wagner M., Goecke R., Evaluating AAM fitting methods for facial expression recognition, Proc. 3rd International Conference on Affective Computing and Intelligent Interaction Workshops, pp. 1-8, (2009)
  • [10] Li Y., Wang S., Zhao Y., Ji Q., Simultaneous facial feature tracking and facial expression recognition, IEEE Trans. Image Processing, 22, 7, pp. 2559-2573, (2013)
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