Expanding PyAFAR: A Novel Privacy-Preserving Infant AU Detector

Itir Önal Ertugrul, Saurabh Hinduja, Maneesh Bilalpur, Daniel S. Messinger, Jeffrey F. Cohn

Abstract: We enhance PyAFAR11Code will be available on: https:\\affectanalysisgroup.github.io/PyAFARI, an open source, Python-based library for facial action unit detection by introducing a privacy-protected infant AU detector. To prevent reconstruction of the training images, we train the infant AU detector by extracting histogram of gradients (HoG) features and using an efficient Light Gradient Boosting Machine (LightGBM) classifier. Models are trained with two large, well-annotated databases. The performance of our approach is comparable to previously developed deep models that have not been released due to privacy concerns. Our models are available for use and further fine-tuning, contributing to the advancement of facial action unit detection.