Efficient CNN Approach for Facial Expression Recognition

Abstract In the last decade, the Facial Expression Recognition field has been studied widely and become the base for many researchers, and still challenging in computer vision. Machine learning technique used in facial expression recognition facing many problems, since human emotions expressed differently from one to another. Nevertheless, Deep learning that represents a novel area of research within machine learning technology has the ability for classifying people’s faces into different emotion classes by using a Deep Neural Network (DNN). The Convolution Neural Network (CNN) method has been used widely and proved as very efficient in the facial expression recognition field. In this study, a CNN technique for facial expression recognition has been presented. The performance of this study has been evaluated using the fer2013 dataset, the total number of images has been used. The accuracy of each epoch has been tested which is trained on 29068 samples, validate on 3589 samples. The overall accuracy of 69.85% has been obtained for the proposed method.

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Dual-modality spatiotemporal feature learning for spontaneous facial expression recognition in e-learning using hybrid deep neural network

The Visual Computer ◽

10.1007/s00371-019-01660-3 ◽

2019 ◽

Vol 36 (4) ◽

pp. 743-755 ◽

Cited By ~ 4

Author(s):

Xiaoliang Zhu ◽

Zijian Chen

Keyword(s):

Neural Network ◽

Facial Expression ◽

Facial Expression Recognition ◽

Deep Neural Network ◽

Feature Learning ◽

Expression Recognition ◽

Dual Modality ◽

Spatiotemporal Feature ◽

E Learning

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Comparison Between Deep Learning Models and Traditional Machine Learning Approaches for Facial Expression Recognition in Ageing Adults

Journal of Computer Science and Technology ◽

10.1007/s11390-020-9665-4 ◽

2020 ◽

Vol 35 (5) ◽

pp. 1127-1146

Author(s):

Andrea Caroppo ◽

Alessandro Leone ◽

Pietro Siciliano

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Facial Expression ◽

Facial Expression Recognition ◽

Expression Recognition ◽

Learning Approaches ◽

Learning Models

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Share Market Prediction using Deep Neural Network

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6447.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 8619-8622

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Reinforcement Learning ◽

Stock Market ◽

Deep Neural Network ◽

Learning Technology ◽

Financial Investment ◽

The Future ◽

Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

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Facial Expression Recognition Based Scoring System for Restaurants using Deep Learning

International Journal of Emerging Science and Engineering ◽

10.35940/ijese.h2467.036820 ◽

2020 ◽

Vol 6 (8) ◽

pp. 6-9

Keyword(s):

Neural Network ◽

Deep Learning ◽

Facial Expression ◽

Convolutional Neural Network ◽

Scoring System ◽

Facial Expression Recognition ◽

Direct Contact ◽

Web Server ◽

Rating System ◽

Expression Recognition

Now a days, the technology has been increased. Hence this is a tool for the advancement of the earlier things. So we are making use of the technology for restaurant scoring system. The fame of unmanned restaurants is the hot topic in society. Because of the absence of the staff, there is no direct contact with the customers to take feedback of the restaurant. This paper represents the automated rating system for the restaurants by detecting the facial emotions with the help of Convolutional Neural Network [CNN] .It consists of web server and a pretrained CNN and detection of facial expression.

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Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200551 ◽

2020 ◽

Author(s):

Masurah Mohamad ◽

Ali Selamat

Keyword(s):

Neural Network ◽

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Missing Data ◽

Machine Learning Technique ◽

Processing Times ◽

Learning Technique ◽

Neural Network Algorithm ◽

Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

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