Regularizing Face Verification Nets For Pain Intensity Regression: https://arxiv.org/abs/1702.06925v3
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.
The fine-tuned regularizered network with a regression layer is tested on the UNBC-McMaster Shoulder-Pain dataset and achieves state-of-the-art performance on pain intensity estimation. The main problem that motivates this work is that expertise is needed to label the pain. The take-home
message is that fine-tuning from a data extensive pre-trained domain can alleviate small training set problems.