Semi-Supervised Approach For Automatic Head Gesture Classification

Abstract

This study utilizes a semi-supervised method, particularly self-training, for automatic head gesture recognition using motion caption data. It explores and compares fully supervised deep learning models and self-training pipelines in terms of their perfor- mance and training approaches. The proposed approach achieved an accuracy score of 52% and a macro F1 score of 44% in the cross validation. Results have shown that leveraging self-training as part of the learning process contributes to improved model performance, due to generating pseudo-labeled data that effectively supplements the original labeled dataset, thereby enabling the model to learn from a larger and more diverse set of training examples.