Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

Taini, Matti; Zhao, Guoying

doi:10.1007/978-3-642-02230-2_25

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The main idea of the Fisher criterion is to learn the weights of each region based on keeping the within-class scatter as small as possible and between-class scatter as large as possible [16,17]. For the C class problem, the similarities of the different samples from the same class form the within-class scatter, while the difference samples from different classes form the between-class scatter.…”

Section: Weights Learned Based On Fisher Criterionmentioning

confidence: 99%

Dynamic biometric identification from multiple views using the GLBP-TOP method

Wang

Shen

Chen

et al. 2014

Bio-Medical Materials and Engineering

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To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions.

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Section: Weights Learned Based On Fisher Criterionmentioning

confidence: 99%

Dynamic biometric identification from multiple views using the GLBP-TOP method

Wang

Shen

Chen

et al. 2014

Bio-Medical Materials and Engineering

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“…In facial expression recognition, it is notable that the facial components take distinct effect on different expressions [13,19,21]. Hence, another major task in this paper is to select the most relevant features among the multiple feature sets extracted from the different facial regions.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, another major task in this paper is to select the most relevant features among the multiple feature sets extracted from the different facial regions. Using boosting algorithm or just simply assign the weight parameters to the corresponding face components [21,28] would be feasible. Recently, the multiple kernels learning (MKL) in support vector machines (SVM) has been introduced to combine heterogeneous sources of information for decision fusion in computer vision.…”

Section: Introductionmentioning

confidence: 99%

Expression Recognition in Videos Using a Weighted Component-Based Feature Descriptor

et al. 2011

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In this paper, we propose a weighted component-based feature descriptor for expression recognition in video sequences. Firstly, we extract the texture features and structural shape features in three facial regions: mouth, cheeks and eyes of each face image. Then, we combine these extracted feature sets using confidence level strategy. Noting that for different facial components, the contributions to the expression recognition are different, we propose a method for automatically learning different weights to components via the multiple kernel learning. Experimental results on the Extended Cohn-Kanade database show that our approach combining component-based spatiotemporal features descriptor and weight learning strategy achieves better recognition performance than the state of the art methods.

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