Regularizing Face Verification Nets For Pain Intensity Regression
This work addresses automated pain assessment from facial expressions, which is important for healthcare applications, but it is incremental as it builds on existing face verification methods.
The paper tackles the problem of limited labeled data for facial expression intensity estimation, specifically for pain assessment, by fine-tuning a pre-trained face verification network with a regularized regression loss and additional expression labels, achieving state-of-the-art performance on the UNBC-McMaster Shoulder-Pain dataset.
Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.