RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition
arXiv:2010.03203
Abstract
Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways. Several methods have been proposed to recognize spontaneous and posed smiles. All follow a feature-engineering based pipeline requiring costly pre-processing steps such as manual annotation of face landmarks, tracking, segmentation of smile phases, and hand-crafted features. The resulting computation is expensive, and strongly dependent on pre-processing steps. We investigate an end-to-end deep learning model to address these problems, the first end-to-end model for spontaneous and posed smile recognition. Our fully automated model is fast and learns the feature extraction processes by training a series of convolution and ConvLSTM layer from scratch. Our experiments on four datasets demonstrate the robustness and generalization of the proposed model by achieving state-of-the-art performances.
arXiv commentAccepted by ACCV
References in corpus (8)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Two-Stream Convolutional Networks for Action Recognition in Videos
- Deep Facial Expression Recognition: A Survey
- Activation Functions: Comparison of trends in Practice and Research for Deep Learning
- Person Re-identification: Past, Present and Future
- Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
- Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning