A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion | Zendy

Abihshek Saini | Zendy; Atanendu Sekhar Mandal | Zendy; R.K. Sharma | Zendy

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A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion

Author(s) -

Abihshek Saini,

Atanendu Sekhar Mandal,

R.K. Sharma

Publication year - 2019

Publication title -

international journal of innovative technology and exploring engineering

Language(s) - English

Resource type - Journals

ISSN - 2278-3075

DOI - 10.35940/ijitee.l3902.1081219

Subject(s) - contourlet , pattern recognition (psychology) , support vector machine , artificial intelligence , computer science , feature (linguistics) , feature extraction , expression (computer science) , gabor transform , computer vision , time–frequency analysis , wavelet transform , linguistics , philosophy , wavelet , programming language , filter (signal processing)

This paper highlights automatic pain expression recognition from facial images of pain. This technique is frequently used in healthcare applications. The research detects the pain expression by an effective recognition method. It is based on the concepts of fusion of contourlet transform with Gabor transform for feature extraction, and classification of expressions by using SVM. The ABC algorithm and GA is implemented for optimizing SVM parameters. In order to authenticate the precision and stoutness of the system, a number of experiments have been done on two databases namely UNBC shoulder pain database and IFD-CEERI database. It's provided throughout the simulation techniques. The suggested strategy is actually effective in recognition of pain expression in terms of accuracy

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