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Pain level estimation in video sequences of face using incorporation of statistical features of frames

Mohebbi Kalkhoran, H ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianMVIP.2015.7397530
  3. Publisher: IEEE Computer Society
  4. Abstract:
  5. Pain level estimation from videos of face has many benefits for clinical applications. Most of the previous works focused only on pain detection task. However, pain level estimation of video sequences has been discussed fewer. In this work, we have proposed a new regression-based approach to estimate the pain level of video sequences. As the first step, facial expression-related features were extracted from each frame, this task was done by reducing identity-related features using the robust principal component analysis decomposition. Then, we used the minimum, maximum, and mean of the features of frames in a sequence to represent that sequence by a fixed-length feature vector. After this, in order to incorporate the discriminant features of pain and reduce the computational complexity, we implemented dimension reduction by Supervised Kernel Locality Preserving Projection (SKLPP) method, and in the end, a linear regression was used to predict the pain level. Experiments on UNBC-McMaster shoulder pain expression archive database show that our method achieves the area under the curve of ROC measure of 88.43 percent in pain detection task that has better result compared to other state of the art methods
  6. Keywords:
  7. Pain level estimation ; Robust principal component analysis (RPCA) ; Computer vision ; Face recognition ; Health ; Image processing ; Linear regression ; Regression analysis ; Video recording ; Area under the curves ; Clinical application ; Dimension reduction ; SKLPP ; State-of-the-art methods ; Statistical features ; Supervised kernel locality preserving projections ; Principal component analysis
  8. Source: 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 172-175 ; 21666776 (ISSN) ; 9781467385398 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7397530