www.tjprc.org editor@tjprc.org FINGERPRINT VERIFICATION USING QUALITATIVE RECKON TO RAISE THE EFFECTUALNESS JASLEEN K. SIDHU & GAGANDEEP SINGH Chandigarh Engineering College, Landran, Mohali, Punjab, India ABSTRACT Biometrics is the currently topic infused in today’s technology at high attention. With identity frauds in the society, one’s unique id is must to keep. Here, a proposed work on fingerprint images using neural networks and fuzzy logic is integrated to enhance the effectualness of the system in naive bayes classifiers. Qualtitave reckon methods are so used as they provide the knowldege and act as result providers in the proposed work. The fingerprint matching is done with neural networks and in naive bayes and the result proves the better use of neural networks. The proposed work using neural networks proves enhancing of the inputs than the existing approaches. KEYWORDS: Fingerprint Images, Fingerprint Minutiae Matching, Fuzzification, Naive Bayes Classifiers, Neural Networks INTRODUCTION Biometrics refers to characteristics of human’s behaviour. Biometrics identification or authentication is used as a form of the access for accurate and reliable identity of a trusted and untrusted user. Fingerprint identification is must as it helps in identifying the unauthorized user or the culprit in civilian applications. The verification of a matched source is done using naive bayes classifiers and also with neural networks. A naive bayes is an indepent feature model. Depending on the probability model, it is `efficient in supervised learning setting. Neural networks along with fuzzy logic is used as an information list to analyze.It is configured through a learning process. Minutiae Matcher The basic concept for minutiae matcher is to take a reference point or line then decide the origin for the co-ordinates and now translate and rotate the whole image to have an image. Combination of patterns called ridges and valleys develop the fingerprints. Single arched section is known the ridges whereas part between two adjoining ridges is known as valley and ridge termination is known as minutiae. Artificial Neural Network(ANN) An Artificial Neural Network (ANN) is an information processing system inspired by the way biological nervous systems, such as the brain, process information. An artificial neuron has many inputs and one output. The neuron has two modes of operation : Training mode, the neuron can be trained to fire (or not), for input patterns. In the using mode, when a taught input pattern is detected at the input, it results to the current output.. Naive Bayes A Bayes classifier is a simple probabilistic classifier based on applying bayes' theorem from bayesian statistics with International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN(P): 2249-6831; ISSN(E): 2249-7943 Vol. 4, Issue 5, Oct 2014, 65-72 © TJPRC Pvt. Ltd.