A Real-Time Automatic Human Facial Expression Recognition System Using Deep Neural Networks

2019 ◽  
pp. 431-441
Author(s):  
Surekha Samsani ◽  
Vineel Abhinav Gottala
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Facial Expression

scholarly journals A SYSTEMATIC REVIEW OF METHODS OF EMOTION RECOGNITION BY FACIAL EXPRESSIONS

2021 ◽  
Vol 9 (5) ◽  
pp. 1141-1152
Author(s):  
Muazu Abdulwakil Auma ◽  
Eric Manzi ◽  
Jibril Aminu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
Scientific Research ◽  
Expression Recognition ◽  
Technical Literature ◽  
Research Results

Facial recognition is integral and essential in todays society, and the recognition of emotions based on facial expressions is already becoming more usual. This paper analytically provides an overview of the databases of video data of facial expressions and several approaches to recognizing emotions by facial expressions by including the three main image analysis stages, which are pre-processing, feature extraction, and classification. The paper presents approaches based on deep learning using deep neural networks and traditional means to recognizing human emotions based on visual facial features. The current results of some existing algorithms are presented. When reviewing scientific and technical literature, the focus was mainly on sources containing theoretical and research information of the methods under consideration and comparing traditional techniques and methods based on deep neural networks supported by experimental research. An analysis of scientific and technical literature describing methods and algorithms for analyzing and recognizing facial expressions and world scientific research results has shown that traditional methods of classifying facial expressions are inferior in speed and accuracy to artificial neural networks. This reviews main contributions provide a general understanding of modern approaches to facial expression recognition, which will allow new researchers to understand the main components and trends in facial expression recognition. A comparison of world scientific research results has shown that the combination of traditional approaches and approaches based on deep neural networks show better classification accuracy. However, the best classification methods are artificial neural networks.

scholarly journals Real Time Facial Expression Recognition System Based on Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4047-4051
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Or Education ◽  
Face Expression ◽  
Active Research ◽  
Main Components

The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..

A Robotic Facial Expression Recognition System Using Real-Time Vision System

2008 ◽  
Vol 381-382 ◽  
pp. 375-378
Author(s):  
K.T. Song ◽  
M.J. Han ◽  
F.Y. Chang ◽  
S.H. Chang
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Vision System ◽  
Recognition System ◽  
Expression Recognition ◽  
On Line ◽  
Processing Platform

The capability of recognizing human facial expression plays an important role in advanced human-robot interaction development. Through recognizing facial expressions, a robot can interact with a user in a more natural and friendly manner. In this paper, we proposed a facial expression recognition system based on an embedded image processing platform to classify different facial expressions on-line in real time. A low-cost embedded vision system has been designed and realized for robotic applications using a CMOS image sensor and digital signal processor (DSP). The current design acquires thirty 640x480 image frames per second (30 fps). The proposed emotion recognition algorithm has been successfully implemented on the real-time vision system. Experimental results on a pet robot show that the robot can interact with a person in a responding manner. The developed image processing platform is effective for accelerating the recognition speed to 25 recognitions per second with an average on-line recognition rate of 74.4% for five facial expressions.

scholarly journals Facial Expression Analysis using Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 1142-1146
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Expression Analysis ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Real Time System ◽  
Facial Expression Analysis

Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.

A Face Based Real Time Communication for Physically and Speech Disabled People

2013 ◽  
pp. 1434-1460
Author(s):  
Ong Chin Ann ◽  
Marlene Valerie Lu ◽  
Lau Bee Theng
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Time Behaviour ◽  
The Disabled ◽  
Detection Stage

The main purpose of this research is to enhance the communication of the disabled community. The authors of this chapter propose an enhanced interpersonal-human interaction for people with special needs, especially those with physical and communication disabilities. The proposed model comprises of automated real time behaviour monitoring, designed and implemented with the ubiquitous and affordable concept in mind to suit the underprivileged. In this chapter, the authors present the prototype which encapsulates an automated facial expression recognition system for monitoring the disabled, equipped with a feature to send Short Messaging System (SMS) for notification purposes. The authors adapted the Viola-Jones face detection algorithm at the face detection stage and implemented template matching technique for the expression classification and recognition stage. They tested their model with a few users and achieved satisfactory results. The enhanced real time behaviour monitoring system is an assistive tool to improve the quality of life for the disabled by assisting them anytime and anywhere when needed. They can do their own tasks more independently without constantly being monitored physically or accompanied by their care takers, teachers, or even parents. The rest of this chapter is organized as follows. The background of the facial expression recognition system is reviewed in Section 2. Section 3 is the description and explanations of the conceptual model of facial expression recognition. Evaluation of the proposed system is in Section 4. Results and findings on the testing are laid out in Section 5, and the final section concludes the chapter.

Sign in / Sign up

Export Citation Format

Share Document