LSTMによる動画からの感情推定
Emotion estimation from video footage with LSTM
MediaPipeが生成する顔のブレンドシェイプをLSTMで処理し、動画から主要な感情を推定する手法を提案。FER2013で71%の精度を達成しつつ計算コストを大幅に削減した。
著者: Samer Attrah
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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
