scholarly journals Feature Fusion In Multimodal Emotion Recognition System For Enhancement Of Human-Machine Interaction

2021 ◽  
Vol 1084 (1) ◽  
pp. 012004
Author(s):  
S Veni ◽  
R Anand ◽  
DIVYA MOHAN ◽  
ELDHO PAUL
Keyword(s):  
Emotion Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Human Machine Interaction ◽  
Multimodal Emotion Recognition ◽  
Machine Interaction

scholarly journals Automatic Multiface Expression Recognition Using Convolutional Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 1-13
Author(s):  
Padmapriya K.C. ◽  
Leelavathy V. ◽  
Angelin Gladston
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Behavior Understanding ◽  
Human Behavior Understanding ◽  
Expression Classification ◽  
Machine Interaction

The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.

scholarly journals Databases, Features and Classification Techniques for Speech Emotion Recognition

2020 ◽  
Vol 9 (6) ◽  
pp. 185-190
Keyword(s):  
Emotion Recognition ◽  
Research Area ◽  
Research Field ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Human Machine Interaction ◽  
Classification Techniques ◽  
Classification Feature ◽  
Physical Gestures ◽  
Machine Interaction

Emotion recognition is a rapidly growing research field. Emotions can be effectively expressed through speech and can provide insight about speaker’s intentions. Although, humans can easily interpret emotions through speech, physical gestures, and eye movement but to train a machine to do the same with similar preciseness is quite a challenging task. SER systems can improve human-machine interaction when used with automatic speech recognition, as emotions have the tendency to change the semantics of a sentence. Many researchers have contributed their extremely impressive work in this research area, leading to development of numerous classification, feature selection, feature extraction and emotional speech databases. This paper reviews recent accomplishments in the area of speech emotion recognition. It also present a detailed review of various types of emotional speech databases, and different classification techniques which can be used individually or in combination and a brief description of various speech features for emotion recognition.

Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks

2019 ◽  
Vol 33 (11) ◽  
pp. 1940015 ◽  
Author(s):  
Hai-Duong Nguyen ◽  
Soonja Yeom ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang ◽  
In-Seop Na ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Research Area ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Network Connections ◽  
Multi Level ◽  
Machine Interaction ◽  
Made In

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

Smart IoT Multimodal Emotion Recognition System Using Deep Learning Networks

2021 ◽  
pp. 3-19
Author(s):  
V. J. Aiswaryadevi ◽  
G. Priyanka ◽  
S. Sathya Bama ◽  
S. Kiruthika ◽  
S. Soundarya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Recognition System ◽  
Learning Networks ◽  
Multimodal Emotion Recognition

Deep spatio-temporal feature fusion with compact bilinear pooling for multimodal emotion recognition

2018 ◽  
Vol 174 ◽  
pp. 33-42 ◽  
Author(s):  
Dung Nguyen ◽  
Kien Nguyen ◽  
Sridha Sridharan ◽  
David Dean ◽  
Clinton Fookes
Keyword(s):  
Emotion Recognition ◽  
Feature Fusion ◽  
Spatio Temporal ◽  
Multimodal Emotion Recognition ◽  
Temporal Feature

scholarly journals Speech Emotion Recognition System Using Gaussian Mixture Model and Improvement proposed via Boosted GMM

2017 ◽  
Vol 7 (2 (S)) ◽  
pp. 56
Author(s):  
Pavitra Patel ◽  
A. A. Chaudhari ◽  
M. A. Pund ◽  
D. H. Deshmukh
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Training Data ◽  
Speech Emotion Recognition ◽  
Human Beings ◽  
Human Machine Interaction ◽  
Data Set ◽  
Communication Partner

<p>Speech emotion recognition is an important issue which affects the human machine interaction. Automatic recognition of human emotion in speech aims at recognizing the underlying emotional state of a speaker from the speech signal. Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. In this paper, we introduce a boosting algorithm for reliably and accurately estimating the class-conditional GMMs. The resulting algorithm is named the Boosted-GMM algorithm. Our speech emotion recognition experiments show that the emotion recognition rates are effectively and significantly boosted by the Boosted-GMM algorithm as compared to the EM-GMM algorithm.<br />During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This work dependent on the emotion recognition of the human beings from their speech signal<br />Emotion recognition from the speaker’s speech is very difficult because of the following reasons: Because of the existence of the different sentences, speakers, speaking styles, speaking rates accosting variability was introduced. The same utterance may show different emotions. Therefore it is very difficult to differentiate these portions of utterance. Another problem is that emotion expression is depending on the speaker and his or her culture and environment. As the culture and environment gets change the speaking style also gets change, which is another challenge in front of the speech emotion recognition system.</p>

Sign in / Sign up

Export Citation Format

Share Document