Emotion estimation from video footage with LSTM.

IF 3.5 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Frontiers in Neurorobotics Pub Date : 2026-02-06 eCollection Date: 2025-01-01 DOI:10.3389/fnbot.2025.1678984

Samer Attrah

引用次数: 0

Abstract

Emotion estimation is a field that has been studied for a long time, and several approaches using machine learning models exist. This article presents BlendFER-Lite, an LSTM model that uses Blendshapes from the MediaPipe library to analyze facial expressions detected from a live-streamed camera feed. This model is trained on the FER2013 dataset and achieves 71% accuracy and an F1-score of 62%, meeting the accuracy benchmark for the FER2013 dataset while significantly reducing computational costs compared to current methods. For the sake of reproducibility, the code repository, datasets, and models proposed in this paper, in addition to the preprint, can be found on Hugging Face at: https://huggingface.co/papers/2501.13432.

Jel classification: D8, H51.

Msc classification: 35A01, 65L10, 65L12, 65L20, 65L70.

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基于LSTM的视频片段情感估计。

情绪估计是一个已经研究了很长时间的领域，并且存在几种使用机器学习模型的方法。本文介绍了BlendFER-Lite，这是一个LSTM模型，它使用MediaPipe库中的Blendshapes来分析从实时流媒体摄像机馈送中检测到的面部表情。该模型在FER2013数据集上进行训练，准确率达到71%，f1得分为62%，满足FER2013数据集的准确率基准，同时与现有方法相比显著降低了计算成本。为了再现性，本文中提出的代码库、数据集和模型，以及预印本，可以在以下网站上找到：https://huggingface.co/papers/2501.13432.Jel分类：D8， H51。Msc分类：35A01, 65L10, 65L12, 65L20, 65L70。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.