Finding Useful Features for Facial Expression Recognition and Intensity Estimation by Neural Network

2020 ◽  
Vol 8 (2) ◽  
pp. 68-84
Author(s):  
Naoki Imamura ◽  
Hiroki Nomiya ◽  
Teruhisa Hochin
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Features ◽  
Expression Recognition ◽  
Intensity Estimation ◽  
Expression Intensity ◽  
The Neural Network

Facial expression intensity has been proposed to digitize the degree of facial expressions in order to retrieve impressive scenes from lifelog videos. The intensity is calculated based on the correlation of facial features compared to each facial expression. However, the correlation is not determined objectively. It should be determined statistically based on the contribution score of the facial features necessary for expression recognition. Therefore, the proposed method recognizes facial expressions by using a neural network and calculates the contribution score of input toward the output. First, the authors improve some facial features. After that, they verify the score correctly by comparing the accuracy transitions depending on reducing useful and useless features and process the score statistically. As a result, they extract useful facial features from the neural network.

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).

scholarly journals Blurred Facial Expression Recognition System by Using Convolution Neural Network

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 804-816
Author(s):  
Elaf J. Al Taee ◽  
Qasim Mohammed Jasim
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Cognitive Activity ◽  
Recognition System ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions

A facial expression is a visual impression of a person's situations, emotions, cognitive activity, personality, intention and psychopathology, it has an active and vital role in the exchange of information and communication between people. In machines and robots which dedicated to communication with humans, the facial expressions recognition play an important and vital role in communication and reading of what is the person implies, especially in the field of health. For that the research in this field leads to development in communication with the robot. This topic has been discussed extensively, and with the progress of deep learning and use Convolution Neural Network CNN in image processing which widely proved efficiency, led to use CNN in the recognition of facial expressions. Automatic system for Facial Expression Recognition FER require to perform detection and location of faces in a cluttered scene, feature extraction, and classification. In this research, the CNN used for perform the process of FER. The target is to label each image of facial into one of the seven facial emotion categories considered in the JAFFE database. JAFFE facial expression database with seven facial expression labels as sad, happy, fear, surprise, anger, disgust, and natural are used in this research. We trained CNN with different depths using gray-scale images from the JAFFE database.The accuracy of proposed system was 100%.

scholarly journals Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network

2011 ◽  
Vol 5 (1) ◽  
Author(s):  
Zaenal Abidin ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
False Positive ◽  
Facial Expression Recognition ◽  
Type I ◽  
Expression Recognition ◽  
Backpropagation Neural Network ◽  
Neural Network Approach ◽  
Image Type

Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15,  for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.

scholarly journals The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Pablo Barros ◽  
Nikhil Churamani ◽  
Alessandra Sciutti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Facial Features ◽  
Expression Recognition ◽  
Current State ◽  
Benchmark Datasets

AbstractCurrent state-of-the-art models for automatic facial expression recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and, thus, improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapts the learned facial features towards the different datasets.

scholarly journals Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 375 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Great Success ◽  
Expression Recognition

As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.

scholarly journals Improving the Accuracy of Automatic Facial Expression Recognition in Speaking Subjects with Deep Learning

2020 ◽  
Vol 10 (11) ◽  
pp. 4002
Author(s):  
Sathya Bursic ◽  
Giuseppe Boccignone ◽  
Alfio Ferrara ◽  
Alessandro D’Amelio ◽  
Raffaella Lanzarotti
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Classification Accuracy ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Video Sequences ◽  
Facial Features ◽  
Expression Recognition ◽  
Lip Reading ◽  
Spatio Temporal

When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model.

scholarly journals Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sharmeen M. Saleem Abdullah ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Video Game ◽  
Facial Expression Recognition ◽  
Emotional Processing ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Expression Recognition

Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

Selective Facial Expression Recognition Using fastICA

2012 ◽  
Vol 433-440 ◽  
pp. 2755-2761 ◽  
Author(s):  
Xiao Hua Zhang ◽  
Zhi Fei Liu ◽  
Ya Jun Guo ◽  
Li Qiang Zhao
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Bp Neural Network ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Facial Features ◽  
Expression Recognition ◽  
Average Recognition Rate ◽  
Neural Network Classifiers

This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.

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