Search Word(s) in Title, Keywords, Authors, and Abstract: Facial Expression Recognition (FER)

Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Deepak Ghimire and Joonwhoan Lee
Page: 443~458, Vol. 10, No.3, 2014
10.3745/JIPS.02.0004
Keywords: Bagging, Ensemble Learning, Extreme Learning Machine, Facial Expression Recognition, Histogram of Orientation Gradient
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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.


Region-Based Facial Expression Recognition in Still Images
Gawed M. Nagi, Rahmita Rahmat, Fatimah Khalid and Muhamad Taufik
Page: 173~188, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.173
Keywords: Facial Expression Recognition (FER), Facial Features Detection, Facial Features Extraction, Cascade Classifier, LBP, One-Vs-Rest SVM
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In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.