用LSTM分析视频面部表情，实现高效情绪识别
Emotion estimation from video footage with LSTM
- 基于MediaPipe提取面部变形参数，输入LSTM模型
- 在FER2013数据集上达71%准确率、62%F1分数
- 计算成本低，适合实时应用，适合做情绪感知系统
情绪估计是长期研究的领域，已有多种机器学习方法。本文提出一种LSTM模型，处理MediaPipe库生成的面部变形参数，对摄像头实时视频中检测到的人脸进行情绪识别。该模型在FER2013数据集上训练，达到71%的准确率和62%的F1分数，符合该数据集的性能基准，同时显著降低计算开销。
原文摘要 · Abstract (English)
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
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