Facial pain expression recognition in real-time videos

Title

Facial pain expression recognition in real-time videos

Creator

Dutta P.; Nachamai M.

Description

Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M.

Source

Journal of Healthcare Engineering, Vol-2018

Date

2018-01-01

Publisher

Hindawi Limited

Coverage

Dutta P., Department of Computer Science, Christ University, Bengaluru, 560029, India; Nachamai M., Siemens India, Bengaluru, 560100, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20402295; PubMed ID: 30510672

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1155/2018/7961427

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062858680&doi=10.1155%2f2018%2f7961427&partnerID=40&md5=eb5f49f43c93843e046372bc5c509380