2015 |
Zhang, Xing; Yin, Lijun; Cohn, Jeffery F Three dimensional binary edge feature representation for pain expression analysis Conference Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on, 1 , 2015, ISBN: 978-1-4799-6026-2. Abstract | Links | BibTeX | Tags: Emotion, facial expression, latent-dynamic conditional random field (LDCRF), pain @conference{Zhang2015b, title = {Three dimensional binary edge feature representation for pain expression analysis}, author = {Xing Zhang and Lijun Yin and Jeffery F Cohn}, url = {http://ieeexplore.ieee.org/document/7163107/}, doi = {10.1109/FG.2015.7163107}, isbn = {978-1-4799-6026-2}, year = {2015}, date = {2015-05-04}, booktitle = {Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on}, volume = {1}, abstract = {Automatic pain expression recognition is a challenging task for pain assessment and diagnosis. Conventional 2D-based approaches to automatic pain detection lack robustness to the moderate to large head pose variation and changes in illumination that are common in real-world settings and with few exceptions omit potentially informative temporal information. In this paper, we propose an innovative 3D binary edge feature (3D-BE) to represent high-resolution 3D dynamic facial expression. To exploit temporal information, we apply a latent-dynamic conditional random field approach with the 3D-BE. The resulting pain expression detection system proves that 3D-BE represents the pain facial features well, and illustrates the potential of noncontact pain detection from 3D facial expression data.}, keywords = {Emotion, facial expression, latent-dynamic conditional random field (LDCRF), pain}, pubstate = {published}, tppubtype = {conference} } Automatic pain expression recognition is a challenging task for pain assessment and diagnosis. Conventional 2D-based approaches to automatic pain detection lack robustness to the moderate to large head pose variation and changes in illumination that are common in real-world settings and with few exceptions omit potentially informative temporal information. In this paper, we propose an innovative 3D binary edge feature (3D-BE) to represent high-resolution 3D dynamic facial expression. To exploit temporal information, we apply a latent-dynamic conditional random field approach with the 3D-BE. The resulting pain expression detection system proves that 3D-BE represents the pain facial features well, and illustrates the potential of noncontact pain detection from 3D facial expression data. |
Publication List
2015 |
Three dimensional binary edge feature representation for pain expression analysis Conference Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on, 1 , 2015, ISBN: 978-1-4799-6026-2. |