Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23472
Title: Region-based feature fusion for facial-expression recognition
Authors: Turan, C
Lam, KM 
Keywords: Canonical correlation analysis
Facial expression recognition
Feature fusion
Local phase quantization
Pyramid of histogram of oriented gradients
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, 7026204, p. 5966-5970 How to cite?
Journal: 2014 IEEE International Conference on Image Processing, ICIP 2014 
Abstract: In this paper, we propose a feature-fusion method based on Canonical Correlation Analysis (CCA) for facial-expression recognition. In our proposed method, features from the eye and the mouth windows are extracted separately, which are correlated with each other in representing a facial expression. For each of the windows, two effective features, namely the Local Phase Quantization (LPQ) and the Pyramid of Histogram of Oriented Gradients (PHOG) descriptors, are employed to form low-level representations of the corresponding windows. The features are then represented in a coherent subspace by using CCA in order to maximize the correlation. In our experiments, the Extended Cohn-Kanade dataset is used; its face images span seven different emotions, namely anger, contempt, disgust, fear, happiness, sadness, and surprise. Experiment results show that our method can achieve excellent accuracy for facial-expression recognition.
URI: http://hdl.handle.net/10397/23472
ISBN: 9.78E+12
DOI: 10.1109/ICIP.2014.7026204
Appears in Collections:Conference Paper

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