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Emotion recognition using facial expressions

TLDR
This project has detected seven emotions of humans which are Happiness, Anger, Sadness, Disgust, Neutral, Surprise and fear, and found that HOG gives a better result the BOF than the Bag-Of-Features.
Abstract
This paper presents emotion recognition using facial expression. Emotion recognition is widely used in industrial applications where emotion of humans are used to derive conclusions on products and detection of suspective behaviour. In this project we have detected seven emotions of humans which are Happiness, Anger, Sadness, Disgust, Neutral, Surprise and fear. We have taken a set of still images, detected the facial region and the features are extracted. Features are extracted using Bag-Of-Features (BOF) and Histogram of Oriented Gradients (HOG). The feature vectors created by these techniques are used to train Support Vector Machines (SVM) and results are verified against a given test input. We have achieved satisfactory results for emotion recognition. We have found that HOG gives a better result the BOF

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Citations
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Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions

TL;DR: The results achieved confirm that the information obtained from facial-expression recognition demonstrated that it is as valid an instrument as that offered by the self-administered questionnaires for the measurement of customer satisfaction.
Journal ArticleDOI

An Evolutionary Optimized Variational Mode Decomposition for Emotion Recognition

TL;DR: Optimized variational mode decomposition is proposed for emotion recognition using single-channel EEG signals using Eigenvector centrality method and an optimum number of modes and penalty factor are selected adaptively for decomposition of non-stationary EEG signals.
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ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

TL;DR: This work presents ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians that outperform current state-of-art algorithms for emotion recognition from 3D gaits.
Proceedings ArticleDOI

Identifying Human Emotions from Facial Expressions with Deep Learning

TL;DR: This work explores the recognition of human facial expressions through a deep learning approach using a Convolutional Neural Network (CNN) algorithm, which can identify seven emotions of the Facial Action Coding System (FACS).
Proceedings ArticleDOI

EmotionalGAN: Generating ECG to Enhance Emotion State Classification

TL;DR: A novel sequence based generative model is proposed to generate ECG samples for enhancing emotion state classification and the average classification accuracy increases around 5% compared with using only original data.
References
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Journal ArticleDOI

Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions

TL;DR: The results achieved confirm that the information obtained from facial-expression recognition demonstrated that it is as valid an instrument as that offered by the self-administered questionnaires for the measurement of customer satisfaction.
Journal ArticleDOI

An Evolutionary Optimized Variational Mode Decomposition for Emotion Recognition

TL;DR: Optimized variational mode decomposition is proposed for emotion recognition using single-channel EEG signals using Eigenvector centrality method and an optimum number of modes and penalty factor are selected adaptively for decomposition of non-stationary EEG signals.
Posted Content

ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

TL;DR: This work presents ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians that outperform current state-of-art algorithms for emotion recognition from 3D gaits.
Proceedings ArticleDOI

Identifying Human Emotions from Facial Expressions with Deep Learning

TL;DR: This work explores the recognition of human facial expressions through a deep learning approach using a Convolutional Neural Network (CNN) algorithm, which can identify seven emotions of the Facial Action Coding System (FACS).
Proceedings ArticleDOI

EmotionalGAN: Generating ECG to Enhance Emotion State Classification

TL;DR: A novel sequence based generative model is proposed to generate ECG samples for enhancing emotion state classification and the average classification accuracy increases around 5% compared with using only original data.
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