arXiv:2501.13432
공식 코드 · Papers with Code
Emotion estimation from video footage with LSTM
원문 초록
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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ExpNet: Landmark-Free, Deep, 3D Facial Expressions
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
0/1 Deep Neural Networks via Block Coordinate Descent
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition
2D+3D facial expression recognition via embedded tensor manifold regularization
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition
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