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

Content Provider	IEEE Xplore Digital Library
Author	Kalkhoran, H.M. Fatemizadeh, E.
Copyright Year	2015
Description	Author affiliation: Electr. Eng. Dept., Sharif Univ. of Technol., Tehran, Iran (Kalkhoran, H.M.; Fatemizadeh, E.)
Abstract	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.
Starting Page	172
Ending Page	175
File Size	600631
Page Count	4
File Format	PDF
ISBN	9781467385398
ISSN	21666784
DOI	10.1109/IranianMVIP.2015.7397530
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-18
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines SKLPP Laplace equations Linear regression Pain level estimation Robust principal component analysis (RPCA) Feature extraction Silicon Matrix decomposition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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