Regularizing Face Net For Discrete-Valued Pain Regression

Limited annotated data is available for the research of estimating facial expression intensities. For example, the ability to train deep CNNs for automated pain assessment is limited by small datasets associated with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain such as face verification can alleviate the problem. In this paper, we propose a regularized network that fine-tunes a state-of-the-art face verification network using expression-intensity labeled data with a regression layer. In this way, the expression regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularizered deep regressor is applied in estimating the intensity of pain intensity estimation (Shoulder-Pain dataset). It achieves the state-of-the-art performance on Shoulder-Pain dataset. Particularly for Shoulder-Pain with the imbalance issue of different pain intensities, a novel weighted evaluation metric is proposed.