iosr-JCE   Volume-15 ~ Issue-1

Abstract: Fingerprint recognition is one of the most popular and successful methods used for person identification which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae which are points where a extracts the ridges and bifurcation from a fingerprint image. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality. Fingerprint image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations. Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various distortions introduced during the acquisition process. In this paper we have used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images. Out of which we have preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint image and tried to improve the quality of images. The Resultant images quality is verified by using different quality measures.

Keywords: minutiae extraction, extracts the ridges and bifurcation, rural fingerprint authentication..

[1] BabasahebV. Bhalerao, Ramesh R. ManzaYogesh M. Rajput Department of CSand IT, Dr.BabasahebAmbedkar Marthwada University, Aurangabad.(MS), India" Use of Quality Measures for Rural Indian Fingerprint Image Database Enhancement and Improve the Recognition Rate International Journal of Computer Applications (0975– 8887) Volume 70– No.18, May 2013.

[2] M. Vatsa, R. Singh, S. Bharadwaj, H. Bhatt, R. Mashruwala "Analyzing Fingerprints of Indian Population Using Image Quality": A UIDAI Case Study 1 - IIIT Delhi, India 2 - UIDAI, India.

[3] C. Puri, K. Narang, A. Tiwari, M. Vatsa, and R. Singh,"On Analysis of Rural and Urban Indian Fingerprint Images" IIIT Delhi, India.

[4] D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, "Hand-book of fingerprint recognition". Springer Verlag, 2003.

[5] R. M.Mandi,S. S. Lokhande, Rotation "Invariant Fingerprint Identification System "International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012). [6] Rohit Singh (Y6400), Utkarsh Shah (Y6510), Vinay Gupta (Y6534) , "Fingerprint Recognition", Department of Computer Science & engineering Indian Institute of technology, Kanpur. Computer Vision and Image. Processing (CS676).

[7] Graig T. Diefenderfer,"Fingerprint Recognition "Naval PostgraduateSchool Monterey, California.

[8] ChiragDadlani, Arun Kumar Passi ,Herman SahotaMitinKrishan Kumar, "Fingerprint recognition using minutiae based feature" As part of EE851: Biometrics.

[9] Mary Lourde R, and DushyantKhosla "Fingerprint Identification in Biometric Security Systems" International Journal of Computer and Electrical Engineering, Vol. 2, No. 5, October, 2010 1793-8163.

[10] D.Maio and D. Maltoni. Direct gray-scale minutiae detection in fingerprints.IEEE Trans. Pattern Anal. And Machine Intell., 19(1):27-40, 1997.

Abstract: Conventional personal identification techniques for instance passwords, tokens, ID card and PIN codes are prone to theft or forgery and thus biometrics isa solution thereto. Biometrics is the way of recognizing and scrutinizing the physical traits of a person. Automated biometrics verification caters as a conducive and legitimate method, but there must be an assurance to its cogency. Furthermore, in most of the cases unimodal biometric recognition is not able to meet the performance requirements of the applications. According to recent trends, recognition based on multimodal biometrics is emerging at a greater pace. Multimodal biometrics unifies two or more biometric traits and thus the issues that emerge in unimodal recognition can be mitigated in multimodal biometric systems. But with the rapid ontogenesis of information technology, even the biometric data is not secure. Digital watermarking is one such technique that is implemented to secure the biometric data from inadvertent or premeditated attacks.This paper propounds an approach that is projected in both the directions of improving the performance of biometric identification system by going multimodal and, increasing the security through watermarking. The biometric traits are initially transformed using Discrete Wavelet and Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction and presented outcomes justifies the effectiveness of the scheme.

Keywords: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Multimodal biometrics, Singular Value Decomposition, Watermarking

[1] A. K. Jain, U. Uludag, Hiding Biometric Data,IEEE Trans. Pattern Analysis and Machine Intelligence, 25(11), Nov. 2003, 1494 – 1498.

[2] A. K. Jain, A. Ross, and S. Prabhakar, An introduction to biometric recognition,IEEE Transactions on Circuits and Systems for Video Technology,14(1),200,4-20.

[3] U. Uludag, S. Pankanti, S. Prabhakar, and A.K. Jain, Biometric cryptosystems: issues and challenges, Proceedings of IEEE, 92( 6),2004, 948-960.

[4] Y. Dodis, L. Reyzin, and A. Smith, Fuzzy extractors: how to generate strong keys from biometrics and other noisy data, Eurocrypt2004, 523-540.

[5] A.Nagar, K.Nandakumar, A. K.Jain, Multibiometric Cryptosystems Based on Feature-Level Fusion, IEEETrans. Inf. Forensics Security, 7( 1), Feb. 2012, 255 – 268

[6] Z. huiming, Z.Huile, A technology of hiding fingerprint minutiae in image,Research & progress of solid state electronics, 26(2),2006, 197-200. [7] C. I. Podilchuk and E. J. Delp, Digital Watermarking: Algorithms and Applications, IEEE Signal Processing Magazine, July 2001, 33-46. [8] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking (Morgan Kaufmann Publishers, 2002).

[9] E. T. Lin, A. M. Eskicioglu, R. L. Lagendijk and E. J. Delp, Advances in Digital Video Content Protection, Proceedings of the IEEE, Special Issue on Advances in Video Coding and Delivery, 2004.

[10] G.C. Langelaar, I. Setyawan, R.I. Lagendijik, Watermarking digital image and video data, IEEE Signal Processing Magazine 17 (5) 2000, 20–46.

Abstract: Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fibre layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using an Haralick Texture Features from digital fundus images. K Nearest Neighbors (KNN) classifiers are used to perform supervised classification. Our results demonstrate that the Haralick Texture Features has Database and classification parts, in Database the image has been loaded and Gray Level Co-occurrence Matrix (GLCM) and thirteen haralick features are combined to extract the image features, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 98%. The impact of training and testing is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.

Keywords: Glaucoma, Haralick Texture features, KNN Classifiers, Feature Extraction

[1] R. Haralick, K. Shanmugam, and I. Dinstein, (1973) "Textural Features for Image Classification", IEEE Trans. on Systems, Man and Cybernetics, SMC–3(6):610–621
[2] Sumeet Dua, Senior Member, IEEE, U. Rajendra Acharya, Pradeep Chowriappa "Wavelet-Based Energy Features for Glaucomatous Image Classification" VOL. 16, NO. 1, JANUARY 2012
[3] U.Rajendra Acharya, Sumeet Due, Xian Du,and Vinitha Sree S "Automated Diagnosis of Glaucoma Using Textural and Higher Order Spectra Features"
[4] R. C. Gonzales, R. E.Woods, and S. L. Eddins. Digital Image Processing Using MATLAB.
[5] J. M. Miquel-Jimenez et al., "Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram," Med. Eng. Phys., vol. 32, pp. 617–622, 2010
[6] Bino Sebastian V, A. Unnikrishnan and Kannan Balakrishnan "grey level co-occurrence matrices: generalisation and some new features" (IJCSEIT), Vol.2, No.2, April 2012
[7] F. I. Alam, R. U. Faruqui, (2011) "Optimized Calculations of Haralick Texture Features", European Journal of Scientific Research, Vol. 50 No. 4, pp. 543-553
[8] Celina Rani George "Glaucomatous Image Classification Using Wavelet Based Energy Signatures And Neural Networks" (IJERT) ISSN: 2278-0181 Vol. 2 Issue 3, March – 2013
[9] Miguel-Jiménez J M , Blanco R, "Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram," Med. Eng. Phys., vol. 32, pp. 617–622, 2010.
[10] P. C. Chen and T. Pavlidis, "Segmentation of texture using correlation," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, pp. 64–69, Jan. 1983.