Phase congruency image and sparse classifier for newborn classifying pain state

Author

Mansor, Muhammad Naufal ; Rejab, Mohd Nazri

Author_Institution

Sch. of Mehatronic Eng., Univ. Malaysia Perlis, Arau, Malaysia

fYear

2013

fDate

Nov. 29 2013-Dec. 1 2013

Firstpage

450

Lastpage

454

Abstract

Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate.

Keywords

Bayes methods; emotion recognition; face recognition; image classification; medical disorders; medical image processing; paediatrics; image analysis; infant pain disorder; local binary pattern; naïve Bayes classifier; newborn classifying pain state; pathological faces; phase congruency image classifier; sparse classifier; Conferences; Databases; Feature extraction; Noise level; Pain; Pediatrics; Phase measurement; Infant Pain; Local Binary Pattern; Naïve Bayes Classifier; Phase congruency image; Sparse Classifier;

fLanguage

English

Publisher

ieee

Conference_Titel

Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on

Conference_Location

Mindeb

Print_ISBN

978-1-4799-1506-4

Type

conf

DOI

10.1109/ICCSCE.2013.6720007

Filename

6720007