An Emotional Recognition System for Facial Expressions with Surface Common Features

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

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Separable convolutional neural networks for facial expressions recognition

Journal Of Big Data ◽

10.1186/s40537-021-00522-x ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Andry Chowanda

Keyword(s):

Neural Networks ◽

Social Interactions ◽

Facial Expressions ◽

Convolutional Neural Networks ◽

The Other ◽

Computational Power ◽

Facial Cues ◽

Emotional Recognition ◽

Testing Accuracy ◽

Facial Expressions Recognition

AbstractSocial interactions are important for us, humans, as social creatures. Emotions play an important part in social interactions. They usually express meanings along with the spoken utterances to the interlocutors. Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor. Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition. However, most of the current models of convolutional neural networks require an enormous computational power to train and process emotional recognition. This research aims to build compact networks with depthwise separable layers while also maintaining performance. Three datasets and three other similar architectures were used to be compared with the proposed architecture. The results show that the proposed architecture performed the best among the other architectures. It achieved up to 13% better accuracy and 6–71% smaller and more compact than the other architectures. The best testing accuracy achieved by the architecture was 99.4%.

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Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

Intelligent Computer Vision and Image Processing ◽

10.4018/978-1-4666-3906-5.ch004 ◽

2013 ◽

pp. 42-55

Author(s):

Yi Ji ◽

Khalid Idrissi

Keyword(s):

Feature Extraction ◽

Facial Expression ◽

Facial Expressions ◽

Facial Expression Recognition ◽

Recognition System ◽

Video Sequences ◽

Polynomial Kernel ◽

Expression Recognition ◽

Emotional States ◽

Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

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Affective Tutoring System for Better Learning

Combining E-Learning and M-Learning ◽

10.4018/978-1-60960-481-3.ch009 ◽

2011 ◽

pp. 134-150 ◽

Cited By ~ 1

Author(s):

Abdolhossein Sarrafzadeh ◽

Samuel T.V. Alexander ◽

Jamshid Shanbehzadeh

Keyword(s):

Facial Expression ◽

Facial Expressions ◽

Recognition System ◽

Expression Recognition ◽

Tutoring System ◽

Tutoring Systems ◽

System A ◽

Facial Expression Analysis ◽

Animated Agent ◽

Case Based

Intelligent tutoring systems (ITS) are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors are expected to be able to take into account the emotional state of students. This paper presents research on the development of an Affective Tutoring System (ATS). The system called “Easy with Eve” adapts to students via a lifelike animated agent who is able to detect student emotion through facial expression analysis, and can display emotion herself. Eve’s adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents an analysis of facial expressions of students engaged in learning with human tutors and how a facial expression recognition system, a life like agent and a case based system based on this analysis have been integrated to develop an ATS for mathematics.

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

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.381-382.375 ◽

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.

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Building the Facial Expressions Recognition System Based on RGB-D Images in High Performance

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-662-49390-8_37 ◽

2016 ◽

pp. 377-387 ◽

Cited By ~ 1

Author(s):

Trung Truong ◽

Ngoc Ly

Keyword(s):

Facial Expressions ◽

High Performance ◽

Recognition System ◽

Facial Expressions Recognition

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Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418560049 ◽

2017 ◽

Vol 32 (04) ◽

pp. 1856004 ◽

Cited By ~ 2

Author(s):

M. Sultan Zia ◽

Majid Hussain ◽

M. Arfan Jaffar

Keyword(s):

Facial Expression ◽

Facial Expressions ◽

Incremental Learning ◽

Facial Expression Recognition ◽

Expression Patterns ◽

Training Session ◽

Recognition System ◽

High Accuracy ◽

Training Data ◽

Expression Recognition

Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expression patterns which cannot be generated and used as training data in single training session. A facial expression recognition system can maintain its high accuracy and robustness globally and for a longer period if the system possesses the ability to learn incrementally. We also propose a novel classification algorithm for multinomial classification problems. It is an efficient classifier and can be a good choice for base classifier in real-time applications. We propose a facial expression recognition system that can learn incrementally. We use Local Binary Pattern (LBP) features to represent the expression space. The performance of the system is tested on static images from six different databases containing expressions from various cultures. The experiments using the incremental learning classification demonstrate promising results.

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