Author : S. Sri Gowthamy 1
Date of Publication :22nd March 2018
Abstract: In recent years, fingerprints are measured to be the finest method for biometric classification. These patterns are secure to use, distinctive for every person and do not change in one’s lifetime. Human fingerprints are well-off in information called minutiae, as per the individuality of the fingerprint it provides us methods of flawless detection. This work is based on the pattern type identification using features that are extracted based on minutia and are classified based on the classifiers of Neural Networks. In this paper, the images are preprocessed using thinning, termination, bifurcation, ridge to valley area process that are to obtain the final minutiae. Featured values such as mean and standard deviation are extracted from the preprocessed image. Then the classification process is done to determine which pattern is the inputted fingerprint based on their values. Classification information is essentially concerned with line patterns, whereas individual information is based on a straight or curved continuous ridgeline. The Classification processes are done using the K Nearest Neighbor Classifier using the outputted image obtained from the preprocessing and the feature extraction values. In the classification scheme, arches pattern loops pattern Whorls pattern were identified from the fingerprint images. The fingerprint classification defines the count of the pattern from the collection of images. The experimental result of the study is fully functional on the minutiae-based method and the values for identifying the different patterns
Reference :
-
- RAVI. J, K. B. RAJA and VENUGOPAL. K. R,” Fingerprint Recognition Using Minutia Score Matching”, International Journal of Engineering Science and Technology Vol.1(2), 2009, 35-42.
- Neeraj Bhargava ,Prafull Narooka and, Minaxi Cotia,”Fingerprint Recognition Using Minutia Matching”, International Journal of Computer Trends and Technologyvolume3Issue4- 2012.
- Mridula and Priyanka,” A Review on Classification of Fingerprint Images”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278- 2834,p- ISSN: 2278-8735.Volume 9, Issue 3, Ver. III (May - Jun. 2014), PP 61-66.
- Ching-Tang Hsieh1, Shys-Rong Shyu1 and Kuo-Ming Hung, ”An Effective Method for Fingerprint Classification”, Tamkang Journal of Science and Engineering, Vol. 12, No. 2, pp. 169_182 (2009).
- Ravi Subban and Dattatreya P. Mankame,” A Study of Biometric Approach Using FingerprintRecognition”, Lecture Notes on Software Engineering, Vol. 1, No. 2, May 2013.
- Ritika Dadhwal and Ajmer Singh“Comparison between feature extraction technique for fingerprint based gender classification using K Nearest Neighbor(knn) classifier“,I J C T International SciencePress A, 9(11) 2016, pp. 5419-5426.
- Ghazali Sulong, Alaa Ahmed Abbood, ” Fingerprint Classification Techniques: A Review”, IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 1, No 1, January 2014.