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Probabilistic Neural Network with GLCM and Statistical Measurements for Increasing Accuracy of Iris Recognition System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 136 - Number 12 |
| Year of Publication: 2016 |
| Authors: Dhia Alzubaydi, Shyma Akram Alrubaie |
10.5120/ijca2016908628
|
Dhia Alzubaydi, Shyma Akram Alrubaie . Probabilistic Neural Network with GLCM and Statistical Measurements for Increasing Accuracy of Iris Recognition System. International Journal of Computer Applications. 136, 12 ( February 2016), 44-51. DOI=10.5120/ijca2016908628
Abstract
The main advantage of biometric system in security mode is either in verification process or identification process for the persons. Iris recognition is one of the fast, accurate, reliable and secure biometric techniques for human identification. It provides automatic authentication of an individual based on the characteristics and unique features in iris structure. Thus the most important step in biometric system is the method of extract feature from pattern, especially in using Artificial Neural Networks (ANN) in the matching (recognition) process. There will be a close relationship between the type of network used and the method of extracting features. In this paper, three method of features extraction is tested using three types of GLCM based on number of angles for each type (2-Ang, 3-Ang, 4-Ang) as First Order Statistics (FOS) and 10 statistical measures as Second Order Statistics (SOS) for each type with three models of PNN, so as the model created is dependent on number of classes (20, 25, 30) in each model. Experimental results proved that third method (4ang-GLCM) of feature extraction with higher trained classes (30) had given best Recognition Rate with accuracy 94.43%. Thus, experimental results have been indicated to the efficiency of the proposed system in recognition accuracy in comparison with the previous methods.
References
- R. H. Abiyev and K. Altunkaya, “Personal Iris Recognition Using Neural Network", International Journal of Security and its Applications, Vol. 2, No. 2, April 2008.
- C. Jayachandra and H. V. Reddy, “Iris Recognition based on pupil using Canny Edge Detection and K-Mean algorithm", International Journal of Engineering and Computer science ISSN: 2319-7242, Vol. 2, No. 1, Pp. 221-225, 1 Jan 2013.
- T. Karthikeyan, “Efficient Bio Metric IRIS Recognition System Using Fuzzy Neural Network", Int. J.of Advanced Networking and Application, Vol. 01, No. 06, Pp. 371-376, 2010.
- A. K. Jain, A. Ross and S. Prabhakar, “An Introduction to Biometric Recognition", IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, Vol.14, No. 1, Jan. 2004.
- M. Elgamal and N. Al-Biqami, “An Efficient Feature Extraction Method for Iris Recognition based on Wavelet Transformation", International Journal of Computer and Information Technology ISSN: 2297-0764, Vol. 02, No. 03, 1May 2013.
- A. Murugan and G. Savithiri, “Fragmented Iris Recognition System using BPNN", International Journal of Computer Applications, Vol. 36-No.4, Dec. 2011.
- L. Yang, F. Albregtsen, T. Lgnnestad, and P. Grgttum, " Methods to Estimate Areas and Perimeters of Blob-Like Objects: A Comparison", IAPR Workshop on Machine Vision Applications, Pp.13-15, 1994.
- K. Roy, P. Bhattacharya and R. C. Debnath, “Multi-Class SVM Based Iris Recognition", Computer and information technology, iccit2007. 10th international conference on. IEEE, Pp. 1-6, 2007.
- M. Vatsa, R. Singh and A. Noore, “Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Features", International Journal of Signal Processing, ISSN: 1304 - 4494, Vol. 2, No.2, 2005.
- S. V. Sheela, and P. A. Vijaya, "Iris Recognition Methods - Survey" International Journal of Computer Applications, Vol. 3, No. 5, Pp. 19-25, June 2010.
- B. Pathak, and D. Barooah, "Texture Analysis Based on the Gray-Level Co-Occurrence Matrix Considering Possible Orientations", International Journal of Advanced Research in Electrical; Electronics and Instrumentation Engineering, Vol. 2, No. 9, Sep.2013.
- D. Gadkari, "Image Quality Analysis Using GLCM", thesis submitted to the College of Arts and Sciences / University of Central Florida, 2004.
- D. A. Clausi,"An analysis of co-occurrence texture statistics as a function of grey level quantization", Canadian Journal of remote sensing, Vol. 28, No. 1, pp. 45–62, 2002.
- L. Soh, and C. Tsatsoulis,"Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices", IEEE Transactions On Geoscience And Remote Sensing, Vol. 37, No. 2, Mar. 1999.
- D. A. Kumar, and S. Dhandapani ,"A Bank Cheque Signature Verification System using FFBP Neural Network Architecture and Feature Extraction based on GLCM", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 3, No. 3, 2014.
- R. Low, and R. Togneri. "Speech recognition using the probabilistic neural network." ICSLP, 1998.
- X. Wu, Fang Lu, Bo Wang, and Jingzhi Cheng, "Analysis of DNA sequence pattern using probabilistic neural network model", Journal of Research and Practice in Information Technology, Vol. 37, No. 4, Pp. 353-363, Nov. 2005.
- H. R. Sushma, and R. Sandeep, "Multi Biometric Template Protection using Random Projection and Adaptive Bloom Filter", International Journal of Research in Electronics and Computer Engineering (IJRECE), VOL. 3, No. 2, June 2015.
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