Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
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초록
An extreme learning machine (ELM) is a recently proposed learning algorithmfor a single-layer feed forward neural network. In this paper we studied the ensemble ofELM by using a bagging algorithm for facial expression recognition (FER). Facial expressionanalysis is widely used in the behavior interpretation of emotions, for cognitive science, andsocial interactions. This paper presents a method for FER based on the histogram oforientation gradient (HOG) features using an ELM ensemble. First, the HOG features wereextracted from the face image by dividing it into a number of small cells. A bagging algorithmwas then used to construct many different bags of training data and each of them wastrained by using separate ELMs. To recognize the expression of the input face image, HOGfeatures were fed to each trained ELM and the results were combined by using a majorityvoting scheme. The ELM ensemble using bagging improves the generalized capability ofthe network significantly. The two available datasets (JAFFE and CK+) of facial expressionswere used to evaluate the performance of the proposed classification system. Even theperformance of individual ELM was smaller and the ELM ensemble using a baggingalgorithm improved the recognition performance significantly.
Abstract
An extreme learning machine (ELM) is a recently proposed learning algorithmfor a single-layer feed forward neural network. In this paper we studied the ensemble ofELM by using a bagging algorithm for facial expression recognition (FER). Facial expressionanalysis is widely used in the behavior interpretation of emotions, for cognitive science, andsocial interactions. This paper presents a method for FER based on the histogram oforientation gradient (HOG) features using an ELM ensemble. First, the HOG features wereextracted from the face image by dividing it into a number of small cells. A bagging algorithmwas then used to construct many different bags of training data and each of them wastrained by using separate ELMs. To recognize the expression of the input face image, HOGfeatures were fed to each trained ELM and the results were combined by using a majorityvoting scheme. The ELM ensemble using bagging improves the generalized capability ofthe network significantly. The two available datasets (JAFFE and CK+) of facial expressionswere used to evaluate the performance of the proposed classification system. Even theperformance of individual ELM was smaller and the ELM ensemble using a baggingalgorithm improved the recognition performance significantly.
참고문헌 (37)
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