Towards Federated Learning Driving Technology for Privacy-Preserving Micro-Expression Recognition

Abstract

As mobile devices and sensor technology advance, their role in communication becomes increasingly indispensable. Micro-expression recognition, an invaluable non-verbal communication method, has been extensively studied in human-computer interaction, sentiment analysis, and security fields. However, the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods, raising concerns about serious privacy leakage and data sharing. To address these limitations, we investigate a federated learning scheme tailored specifically for this task. Our approach prioritizes user privacy by employing federated optimization techniques, enabling the aggregation of clients' knowledge in an encrypted space without compromising data privacy. By integrating established micro-expression recognition methods into our framework, we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms. To our knowledge, this marks the first application of federated learning to the micro-expression recognition task.