Incorporating high-level and low-level cues for pain intensity estimation

Pain is a transient physical reaction that exhibits on human faces. Automatic pain intensity estimation is of great importance in clinical and health-care applications. Pain expression is identified by a set of deformations of facial features. Hence, features are essential for pain estimation. In this paper, we propose a novel method that encodes low-level descriptors and powerful high-level deep features by a weighting process, to form an efficient representation of facial images. To obtain a powerful and compact low-level representation, we explore the way of using second-order pooling over the local descriptors. Instead of direct concatenation, we develop an efficient fusion approach that unites the low-level local descriptors and the high-level deep features. To the best of our knowledge, this is the first approach that incorporates the low-level local statistics together with the high-level deep features in pain intensity estimation. Experiments are evaluated on the benchmark databases of pain. The results demonstrate that the proposed low-to-high-level representation outperforms other methods and achieves promising results.

Yang Ruijing, Hong Xiaopeng, Peng Jinye, Feng Xiaoyi, Zhao Guoying

Publication type:
A4 Article in conference proceedings

Place of publication:
2018 24th International Conference on Pattern Recognition (ICPR)

6G Publication


Full citation:
R. Yang, X. Hong, J. Peng, X. Feng and G. Zhao, “Incorporating high-level and low-level cues for pain intensity estimation,” 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, 2018, pp. 3495-3500. doi: 10.1109/ICPR.2018.8545244


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