IPSJ Transactions on Computer Vision and Applications
Online ISSN : 1882-6695
Facial Expression Recognition and Analysis: A Comparison Study of Feature Descriptors
Chun Fui LiewTakehisa Yairi
Author information
JOURNALS FREE ACCESS

Volume 7 (2015) Pages 104-120

Details
Download PDF (4164K) Contact us
Abstract

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.

Information related to the author
© 2015 by the Information Processing Society of Japan
Previous article Next article

Recently visited articles
feedback
Top