Facial Expression Intensity Estimation using Deep Convolutional Neural Network

Facial expression recognition (FER) systems have attracted much research interest in the area of Machine Learning. We designed a large, deep convolutional neural network to classify 40,000 images in the data-set into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN with graphic processing units (GPUs). Optimizing Deep CNN is to reduce training time for system.

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Facial Expression Recognition Using Multi-deep Convolutional Neural Network Encoders with Support Vector Machines

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.5.1059 ◽

2021 ◽

Vol 11 (5) ◽

pp. 345-349 ◽

Cited By ~ 1

Author(s):

Tran Ngoc Dong ◽

◽

Le Van ◽

Pham The Bao

Keyword(s):

Neural Network ◽

Support Vector Machines ◽

Facial Expression ◽

Convolutional Neural Network ◽

Facial Expression Recognition ◽

Deep Convolutional Neural Network ◽

Support Vector ◽

Expression Recognition ◽

Vector Machines

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

PeerJ Computer Science ◽

10.7717/peerj-cs.736 ◽

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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