Recognising Depression Using Facial and Cephalic Feature Extractors with Machine Learning - Answer to BeyondSmile Challenge

This paper introduces a method for depression detection by applying feature extraction techniques to facial and cephalic data from the FacePsy dataset, as part of the ABC2025 Challenge on BeyondSmile: Detecting Depression through Facial Behavior and Head Gestures. Two feature extraction schemes—TSFEL and Catch22—were employed to derive features from Facial Action Units, Euler angles, eye-opening and smiling probabilities, and facial distances and angles computed from landmarks. Moreover, approximate entropy was extracted as a feature. Following feature extraction, feature selection and classification were performed using six machine learning models. In the Leave-One-Patient-Out cross-validation experiment, the Random Forest classifier achieved the best performance with an AUC of 1.00 using 100 combined features, surpassing the original work’s AUC of 0.67. For nested cross-validation with Leave-One-Day-Out and multimodality, the k -NN algorithm with SMOTE obtained an AUC of 0.94, outperforming the original result of 0.81. To enhance clinical interpretability, SHapley Additive explanations values were used to explain model decisions. The proposed approach demonstrates significant promise for depression detection in naturalistic settings using visual cues.