Emotion estimation from video footage with LSTM

Fuente: arXiv
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Autor principal: Attrah, Samer
Formato: Preprint
Publicado: 2025
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author Attrah, Samer
author_facet Attrah, Samer
contents Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-shapes produced by the library MediaPipe, for a face detected in a live stream of a camera, to estimate the main emotion from the facial expressions, this model is trained on the FER2013 dataset and delivers a result of 71% accuracy and 62% f1-score which meets the accuracy benchmark of the FER2013 dataset, with significantly reduced computation costs. https://github.com/Samir-atra/Emotion_estimation_from_video_footage_with_LSTM_ML_algorithm
format Preprint
id arxiv_https___arxiv_org_abs_2501_13432
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Emotion estimation from video footage with LSTM
Attrah, Samer
Computer Vision and Pattern Recognition
Machine Learning
Robotics
68T45 (primary) 68T07, 68T40 (secondary)
I.4.8; J.4; I.2.9
Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-shapes produced by the library MediaPipe, for a face detected in a live stream of a camera, to estimate the main emotion from the facial expressions, this model is trained on the FER2013 dataset and delivers a result of 71% accuracy and 62% f1-score which meets the accuracy benchmark of the FER2013 dataset, with significantly reduced computation costs. https://github.com/Samir-atra/Emotion_estimation_from_video_footage_with_LSTM_ML_algorithm
title Emotion estimation from video footage with LSTM
topic Computer Vision and Pattern Recognition
Machine Learning
Robotics
68T45 (primary) 68T07, 68T40 (secondary)
I.4.8; J.4; I.2.9
url https://arxiv.org/abs/2501.13432