Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition

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

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Cite this article
IEEE Style
Deepak Ghimire and Joonwhoan Lee, "Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition," Journal of Information Processing Systems, vol. 10, no. 3, pp. 443~458, 2014. DOI: 10.3745/JIPS.02.0004.

ACM Style
Deepak Ghimire and Joonwhoan Lee, "Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition," Journal of Information Processing Systems, 10, 3, (2014), 443~458. DOI: 10.3745/JIPS.02.0004.