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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 643Single Scale Retinex for Infant Pain Recognition

Single Scale Retinex for Infant Pain Recognition

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Abstract:

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can’t afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed the Single Scale Retinex (SSR) to remove the illumination level. Secondly, Discrete Cosine Transform (DCT) was adopted as the feature extraction. We determine the condition of the infants (pain/no pain) with Linear Discriminant Analysis (LDA). Several diagnosis tests were performed to estimate the performance of the suggested method under various illumination levels.

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Periodical:

Applied Mechanics and Materials (Volume 643)

Pages:

218-223

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.643.218

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Online since:

September 2014

Authors:

Muhammad Naufal Mansor*, Ahmad Kadri Junoh, Amran Ahmed, Hussin Kamarudin, Azrini Idris

Keywords:

Infant Pain, Linear Discriminant Analysis Classifier, Single Scale Retinex

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] S. C, Chuang, Shih F. Y., and. Slack M. R. Machine recognition and representation of neonatal facial displays of acute pain, Artificial Intelligence in Medicine 36(2), 2006, pp.211-222.

DOI: 10.1016/j.artmed.2004.12.003

[2] S. Brahnam, C. Chuang, S. S Randal, and Y. S Frank. Machine assessment of neonatal facial expressions of acute pain, Decision Support Systems 43, 2007, pp.1242-125.

DOI: 10.1016/j.dss.2006.02.004

[3] S. Brahnam , L. Nanni, and S. Randall. Introduction to neonatal facial pain detection using common and advanced face classification techniques, Advanced Computation Intelligence Paradigms in Healthcare, 1, Studies in Computational Intelligence (SCI) Series: Springer-Verlag, Berlin, 48, 2004, pp.225-253.

DOI: 10.1007/978-3-540-47527-9_9

[4] S. Brahnam, F. C Cgao, Y. S Frank, and R. S Melinda. SVM classification of neonatal facial image of pain, Proceedings of the 6th International Workshop on Fuzzy Login and Applications (WILF05).

[5] S. Brahnam and L. Nanni , Neonatal facial pain detection using NNSOA and LSVM, Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV08), Las Vegas, vol. 2, 2008, pp.352-357.

[6] M. Kirby, and L. Sirovich, (1990). Applications of the Karhunen-Loeve procedure for the characterization of human faces, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp.103-108.

DOI: 10.1109/34.41390

[7] S. Brahnam, C. F. Chuang, F. Y. Shih, and M. R. Slack, SVM classification of neonatal facial images of pain, Fuzzy Logic and Applications, Isabelle Bloch, Alfredo Petrosino, Andrea G. B. Tettamanzi, editors, Lecture Notes in Computer Science, 3849, pp.111-115.

DOI: 10.1007/11676935_15

[8] K. Pun, and Y. Moon, Recent advances in ear biometrics., pp.144-149, (2004).

[9] Jobson, D.J., Rahman, Z., and Woodell, G.A. (1997). Properties and performance of a center/surround retinex, IEEE Transactions on Image Processing, Vol. 6, No. 3, pp.451-462.

DOI: 10.1109/83.557356

[10] Pallabi, P., and Thuraisingham, B. (2006). Face Recognition using Multiple Classifiers, Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) pp.179-186.

DOI: 10.1109/ictai.2006.59

[11] C. Rafael Gonzalez, E. Richard Woods and L. Steven Eddins. Digital Image Processing using MATLAB. Pearson Education. ISBN 978-81-7758-898-9, (2004).

[12] B. Schwerin, and K. K. Paliwal, Local-DCT features for facial recognition, In Proc. Intern. Conf. Signal Proc. and Communication Systems, Gold Coast, Australia, Dec (2008).

DOI: 10.1109/icspcs.2008.4813751

[13] C. Sanderson , Automatic Person Verification Using Speech & Face Information, Dissertation presented to School of Microelectronic Engineering, Griffith University, (2002).

[14] R. Kohavi . A study of cross-validation and bootstrap for accuracy estimation and model selection, In Paper presented at the14th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, (1995).

[15] Ahmed, N., Natarajan, T., and Rao, K.R. (1974). On image processing and a discrete cosine transform. IEEE Transactions on Computers C-23(1): pp.90-93.

DOI: 10.1109/t-c.1974.223784

[16] Chen, W.H., and Pratt, W.K. (1984). Scene adaptive coder. IEEE Transactions on Communications COM-32: pp.225-232.

DOI: 10.1109/tcom.1984.1096066