scholarly journals Emotion recognition using fiducial points via deep learning

2021 ◽  
Author(s):  
Jawad Khan
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Emotional State ◽  
Image Sequences ◽  
Learning Method ◽  
Dynamic Features ◽  
Daily Lives ◽  
Human Ability ◽  
Active Learning Method ◽  
Euclidean Distances

Emotion awareness is critical because of the function that emotions play in our daily lives. As a result, automatic emotion recognition aims to provide a machine with the human ability to interpret and comprehend a person's emotional state in order to predict his intents based on his facial expression. In this research, a new method for improving the accuracy of emotion recognition from facial expression is proposed, which is based solely on input attributes deduced from fiducial points. First, 1200 dynamic features representing the percentage of euclidean distances between facial fiducial points in the first frame and facial fiducial points in the last frame are extracted from image sequences. Second, just the most relevant features are chosen using active learning method. Finally, to categorise facial expression input into emotion, the selected features are provided to a ResNet classifier.

scholarly journals Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 52 ◽  
Author(s):  
Fatima Zahra Salmam ◽  
Abdellah Madani ◽  
Mohamed Kissi
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Feature Selection Method ◽  
Strong Relationship ◽  
Image Sequences ◽  
Dynamic Features ◽  
New Approach ◽  
Human Ability ◽  
Euclidean Distances

The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database.

The Multi-Agents Architecture for Emotion Recognition

2014 ◽  
Vol 2 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Mohamed Néji ◽  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Information Retrieval ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Retrieval System ◽  
Emotional State ◽  
Physiological State ◽  
Information Retrieval System ◽  
User Needs ◽  
Degree Of Satisfaction ◽  
Multi Agents

The author's research focuses on the problem of Information Retrieval System (IRS) that integrates the human emotion recognition. This system must be able to recognize the degree of satisfaction of the user for the result found through its facial expression, its physiological state, its gestures and its voice. This paper is an algorithm for recognizing the emotional state of a user during a search session in order to issue the relevant documents that the user needs. The authors also present the architecture agent of the envisaged system and the organizational model.

scholarly journals Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal

2018 ◽  
Vol 6 (1) ◽  
pp. 1-20
Author(s):  
Tahirou Djara ◽  
Abdoul Matine Ousmane ◽  
Antoine Vianou
Keyword(s):  
Signal Processing ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Emotional State ◽  
Processing Algorithm ◽  
State Recognition ◽  
Physiological Signal ◽  
Emotional Interaction

Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.

scholarly journals Tracking the affective state of unseen persons

2019 ◽  
Vol 116 (15) ◽  
pp. 7559-7564 ◽  
Author(s):  
Zhimin Chen ◽  
David Whitney
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Social Functioning ◽  
Affective State ◽  
Visual Context ◽  
Unique Contribution ◽  
High Agreement ◽  
Human Ability ◽  
Valence And Arousal ◽  
Over Time

Emotion recognition is an essential human ability critical for social functioning. It is widely assumed that identifying facial expression is the key to this, and models of emotion recognition have mainly focused on facial and bodily features in static, unnatural conditions. We developed a method called affective tracking to reveal and quantify the enormous contribution of visual context to affect (valence and arousal) perception. When characters’ faces and bodies were masked in silent videos, viewers inferred the affect of the invisible characters successfully and in high agreement based solely on visual context. We further show that the context is not only sufficient but also necessary to accurately perceive human affect over time, as it provides a substantial and unique contribution beyond the information available from face and body. Our method (which we have made publicly available) reveals that emotion recognition is, at its heart, an issue of context as much as it is about faces.

scholarly journals Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal

2020 ◽  
pp. 1946-1967
Author(s):  
Tahirou Djara ◽  
Abdoul Matine Ousmane ◽  
Antoine Vianou
Keyword(s):  
Signal Processing ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Emotional State ◽  
Processing Algorithm ◽  
State Recognition ◽  
Physiological Signal ◽  
Emotional Interaction

Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method
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