mlsanityData to 7 Oct 2026

Emotion estimation from video footage with LSTM

arXiv 2501.13432 ↗First posted 23 Jan 2025cs.CV cs.LG cs.ROcited by 0

Not on the map. Why?

Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

27 of 67 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2019On the Convergence of Adam and Beyondcited by 834
  6. 2019MediaPipe: A Framework for Building Perception Pipelinescited by 572
  7. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  8. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 301
  9. 2018Deep Facial Expression Recognition: A Surveycited by 269
  10. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  11. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  12. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
15 more
  1. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  2. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  3. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  4. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  5. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  6. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  7. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  8. 2019Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Networkcited by 37
  9. 2016Survey of resampling techniques for improving classification performance in unbalanced datasetscited by 32
  10. 2021Facial Emotion Recognition: State of the Art Performance on FER2013cited by 20
  11. 2023Leveraging TCN and Transformer for effective visual-audio fusion in continuous emotion recognitioncited by 20
  12. 2019Impact of facial landmark localization on facial expression recognitioncited by 7
  13. 2024Emotion Recognition Using Transformers with Masked Learningcited by 4
  14. 2023The MCC approaches the geometric mean of precision and recall as true negatives approach infinitycited by 2
  15. 2024FaceDig: Automated tool for placing landmarks on facial portraits for geometric morphometrics userscited by 2

Built on by

Not cited yet.