Facial expression recognition algorithm based on deep convolution neural network

2017 ◽  
Author(s):  
Leonid Ivanovsky ◽  
Vladimir Khryashchev ◽  
Anton Lebedev ◽  
Igor Kosterin
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Algorithm ◽  
Convolution Neural Network ◽  
Expression Recognition ◽  
Deep Convolution Neural Network

Conditional convolution neural network enhanced random forest for facial expression recognition

2018 ◽  
Vol 84 ◽  
pp. 251-261 ◽  
Author(s):  
Yuanyuan Liu ◽  
Xiaohui Yuan ◽  
Xi Gong ◽  
Zhong Xie ◽  
Fang Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Convolution Neural Network ◽  
Expression Recognition

scholarly journals Multilabel convolution neural network for facial expression recognition and ordinal intensity estimation

2021 ◽  
Vol 7 ◽  
pp. e736
Author(s):  
Olufisayo Ekundayo ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Convolution Neural Network ◽  
Superior Performance ◽  
Expression Recognition ◽  
Intensity Estimation ◽  
Expression Intensity ◽  
Intensity Of Emotion

Facial Expression Recognition (FER) has gained considerable attention in affective computing due to its vast area of applications. Diverse approaches and methods have been considered for a robust FER in the field, but only a few works considered the intensity of emotion embedded in the expression. Even the available studies on expression intensity estimation successfully assigned a nominal/regression value or classified emotion in a range of intervals. Most of the available works on facial expression intensity estimation successfully present only the emotion intensity estimation. At the same time, others proposed methods that predict emotion and its intensity in different channels. These multiclass approaches and extensions do not conform to man heuristic manner of recognising emotion and its intensity estimation. This work presents a Multilabel Convolution Neural Network (ML-CNN)-based model, which could simultaneously recognise emotion and provide ordinal metrics as the intensity estimation of the emotion. The proposed ML-CNN is enhanced with the aggregation of Binary Cross-Entropy (BCE) loss and Island Loss (IL) functions to minimise intraclass and interclass variations. Also, ML-CNN model is pre-trained with Visual Geometric Group (VGG-16) to control overfitting. In the experiments conducted on Binghampton University 3D Facial Expression (BU-3DFE) and Cohn Kanade extension (CK+) datasets, we evaluate ML-CNN’s performance based on accuracy and loss. We also carried out a comparative study of our model with some popularly used multilabel algorithms using standard multilabel metrics. ML-CNN model simultaneously predicts emotion and intensity estimation using ordinal metrics. The model also shows appreciable and superior performance over four standard multilabel algorithms: Chain Classifier (CC), distinct Random K label set (RAKEL), Multilabel K Nearest Neighbour (MLKNN) and Multilabel ARAM (MLARAM).

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