The week's pick
Random Articles
Reseach Article
Designing Fuzzy Inference System to Diagnosis down Syndrome by Face Processing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 24 - Number 3 |
| Year of Publication: 2011 |
| Authors: Mohammad Mojtaba Keikhayfarzaneh, B.Somayeh Mousavi, Javad Khalatbari |
10.5120/2931-3881
|
Mohammad Mojtaba Keikhayfarzaneh, B.Somayeh Mousavi, Javad Khalatbari . Designing Fuzzy Inference System to Diagnosis down Syndrome by Face Processing. International Journal of Computer Applications. 24, 3 ( June 2011), 20-28. DOI=10.5120/2931-3881
Abstract
Various kind of mental illness is common in all parts of the globe, and all age groups. New diagnostic methods will be vital stage in reducing or eliminating these types of disease. In this paper novel fuzzy rule-base system is designed, and programmed by computer software, for diagnosis of Down Syndrom faces in an image. System input is an arbitrary color image. In first step face regions should be selected. An accurate face detection system is utilized which applies skin color, lip position, face shape information and ear texture properties, as the key parameters. After this stage, detected face regions are processing carefully by proposed fuzzy system, and some features such as face area and eye distance are investigating carefully. Finally the probability of being Down Syndrom is revealed by designed system. 98.33% correct detection is obtained applying this algorithm on various image databases. This system could be considered as the first step to device automatic system for diagnosis of illness and would help psychiatry.
References
- Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., Walters, E.E. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6): 593-602, 2005.
- Demyttenaere K, Bruffaerts R, Posada-Villa J, Worldwide prevalence of mental disorders is high but treatment needs are not always met, Evidence-Based Healthcare & Public Health, 2004, pp. 359–361.
- http://www.nimh.nih.gov/index.shtml
- Sander L. Gilman, Seeing the Insane (New York: John Wiley & Sons, 1982).
- Janet Browne, “Darwin and the face of madness,” The Anatomy of Madness: Essays in the History ofPsychiatry, vol. 1, ed. W.F. Bynum, Roy Porter, and Michael Shepherd (New York: Tavistock, 1985)
- M.Yang, D.J.Kriegman, N.Ahuja, Detecting Face in Image: A Survey, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.1, 2002.
- P. Moallem, B.S. Mousavi, S.A. Monadjemi, A Novel Rule Base System for Pose independent Faces Detection, Applied Soft Computing, 2011, pp. 1801-1810.
- Q. Liu, Y. Ruj, A. Gupta and JJ Cadiz, Automating Camera Management for Lecture Room Environment, In: ACM CHI, 2000.
- S.N.Sivanandum, S.Sumathi, S.N.Deepa, ‘Introduction to Fuzzy Logic Using MATLAB’, (springer-verlag Berlin Heidelberg, 2007)
- S.N.Sivanandam S.N.Deepa, ‘Introduction to Genetic Algorithms’, (Springer-Verlag Berlin Heidelberg, 2008), pp. 165-209
- Agus Priyono, Muhammad Ridwan, Generation of Fuzzy Rules With Subtractive Clustering, Jurnal Teknologi, 2005, pp. 143-153.
- R.Davies, ‘Machine Vision’, (Morgan Kaufman, 2004)
- J.A.Dargham, A.Chekima, ‘Lips Detection in the Normalised RGB Color Scheme’, Information and Communication Technologies, 2006, (1), pp.1546-1551.
- S. Mousavi, P. Moallem, M. Kavosh, Detecting Faces in Colour Images Using Fuzzy Rule Based Classifier, 2nd Joint Congress on Fuzzy and Intelligent Systems, Tehran, Iran, 2008.
- P.Yan, K.W.Boyer, ‘Biometric Recognition Using 3D Ear Shape’, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2007, 29, (8)
- S. Mousavi, P. Moallem, M. Kavosh, A Novel Fuzzy Rule Based Algorithm to Detect Faces with Various Pose, 17th Iranian conference on Electrical engineering, Tehran, Iran, 2009.
- http://www.facedetection.com.
- http://www. vision.caltech.edu.
- http://www.imm.dtu.dk/~aam/aamexplorer
Index Terms
Keywords