Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm

Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analyzed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. This results are compared with the existing for MicroFacial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.

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Blurred Facial Expression Recognition System by Using Convolution Neural Network

Webology ◽

10.14704/web/v17i2/web17068 ◽

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%.

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Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2020.p0792 ◽

2020 ◽

Vol 24 (6) ◽

pp. 792-801

Author(s):

Chang Liu ◽

◽

Kaoru Hirota ◽

Bo Wang ◽

Yaping Dai ◽

...

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Facial Expressions ◽

Affective State ◽

Texture Feature ◽

Service Robots ◽

Facial Features ◽

Expression Recognition

An emotion recognition framework based on a two-channel convolutional neural network (CNN) is proposed to detect the affective state of humans through facial expressions. The framework consists of three parts, i.e., the frontal face detection module, the feature extraction module, and the classification module. The feature extraction module contains two channels: one is for raw face images and the other is for texture feature images. The local binary pattern (LBP) images are utilized for texture feature extraction to enrich facial features and improve the network performance. The attention mechanism is adopted in both CNN feature extraction channels to highlight the features that are related to facial expressions. Moreover, arcface loss function is integrated into the proposed network to increase the inter-class distance and decrease the inner-class distance of facial features. The experiments conducted on the two public databases, FER2013 and CK+, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 72.56% and 94.24%, respectively. The improvement in emotion recognition accuracy makes our approach applicable to service robots.

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Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

Information ◽

10.3390/info10120375 ◽

2019 ◽

Vol 10 (12) ◽

pp. 375 ◽

Cited By ~ 3

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.

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

Journal of Soft Computing and Data Mining ◽

10.30880/jscdm.2021.02.01.006 ◽

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.

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Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure

International Journal of Software Innovation ◽

10.4018/ijsi.2018100108 ◽

2018 ◽

Vol 6 (4) ◽

pp. 103-116

Author(s):

Junqi Guo ◽

Ke Shan ◽

Hao Wu ◽

Rongfang Bie ◽

Wenwan You ◽

...

Keyword(s):

Neural Network ◽

Facial Expression ◽

Convolutional Neural Network ◽

Network Structure ◽

Facial Expression Recognition ◽

Recognition Accuracy ◽

Gaussian White Noise ◽

Feature Representation ◽

Expression Recognition ◽

K Nearest Neighbor

Human facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. In this article, the authors employ a deep convolutional neural network (CNN) to devise a facial expression recognition system to discover deeper feature representation of facial expression. The proposed system is composed of the input module, the pre-processing module, the recognition module and the output module. The authors introduce jaffe and ck+ to simulate and evaluate the performance under the influence of different factors (e.g. network structure, learning rate and pre-processing). The authors also examine the anti-noise property of the system with zero-mean gaussian white noise. In addition, they simulate the recognition accuracy on different expression pairs and discuss the confusion issue on similar expression recognition. Finally, they introduce the k-nearest neighbor (KNN) algorithm compared with CNN to make the results more convincing.

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