2016
DOI: 10.1007/s12193-015-0209-0
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Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

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Cited by 210 publications
(100 citation statements)
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“…For solving the optimization problem (12), the gradient of the first term of the minimization objective function in Equation (12) is formulated as follows…”
Section: Patch Weight Optimizationmentioning
confidence: 99%
See 2 more Smart Citations
“…For solving the optimization problem (12), the gradient of the first term of the minimization objective function in Equation (12) is formulated as follows…”
Section: Patch Weight Optimizationmentioning
confidence: 99%
“…Deep learning with convolutional neural network (CNN) such as multiscale feature based CNN [11], hierarchical committee based CNN [12] and architecture improved CNN [13] has also been applied for static expression recognition. Pramerdorfer and Kampel [14] gave a detailed survey about these algorithms.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Emotish [74] allows the user to snap a selfie and investigate what she is feeling in that moment. Other examples which use similar techniques can be found in References [75][76][77]. Other apps exist that use emotions to provide suggestions or correlations.…”
Section: Emotional Trackingmentioning
confidence: 99%
“…Chen et al 23 used the SimpleMKL method to combine visual and acoustic features. Kim et al 17 proposed a committee machine method to combine 108 CNN models in. Kahou et al 24 proposed a voting matrix and used random search to tune the fusion weight parameters.…”
Section: Related Workmentioning
confidence: 99%