A Study on Machine Learning Approach for Fingerprint Recognition System (original) (raw)
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Overview of Fingerprint Recognition System
— This article is an overview of a current research based on fingerprint recognition system. In this paper we highlighted on the previous studies of fingerprint recognition system. This paper is a brief review in the conceptual and structure of fingerprint recognition. The basic fingerprint recognition system consists of four stages: firstly, the sensor which is used for enrolment & recognition to capture the biometric data. Secondly, the pre-processing stage which is used to remove unwanted data and increase the clarity of ridge structure by using enhancement technique. Thirdly, feature extraction stage which take the input from the output of the pre-processing stage to extract the fingerprint features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. The aim of this paper is to review various recently work on fingerprint recognition system and explain fingerprint recognition stages step by step and give summaries of fingerprint databases with characteristics.
A Study on Fingerprint (biometrics) Recognition
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge. I. INTRODUCTION
Fingerprint Recognition System: Design & Analysis
2011
Fingerprint Recognition is one of the research hotspots in Biometrics. It refers to the automated method of verifying a match between two human fingerprints. It is essentially a challenging pattern recognition problem where two competing error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR) need to be minimized. Advancement of computing capabilities led to the development of Automated Fingerprint Authentication Systems (AFIS) and this led to extensive research especially in the last two decades. In this paper, we attempt to give a comprehensive scoping of the fingerprint recognition problem and address its major design and implementation issues as well as give an insight into its future prospects.
Performance Evaluation of the Automatic Fingerprint Recognition System
The biometric characteristics that are used in the authentication system are unique for each person. The major advantage of the biometrics is that you have always with your way to authenticate yourself. Furthermore biometrics make it possible to know who is authenticated and where. Fingerprint is taken as proffered technique for the biometric authentication because of the public acceptability, ease of use, economy of scale, easy installation and availability. There are two approaches to fingerprint recognition minutia base and image based but in this paper we limit to the minutia based recognition. In this paper we evaluate the minutia as per three stage approach and given the performance evaluation especially for the false minutia and the fingerprint classifiers. The estimation of the previously used scheme and the new scheme is analysed and the results were appreciating.
Fingerprint Classification Using Fuzzy-neural Network and Other Methods
IAES International Journal of Artificial Intelligence (IJ-AI)
The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.
PROPOSAL TO ENHANCE FINGERPRINT RECOGNITION SYSTEM
This research concentrates on particular aspect, that is: Build Fingerprint Recognition System as in traditional but with modest suggested strategy aim to introduce an optimal fingerprint image feature's vector to the person and then considers it to be stored in database for future matching.
A Survey on Fingerprint Recognition System
Fingerprints have been widely accepted throughout the world and is considered to be the most prominent biometric. Several robust techniques have been developed for fingerprint matching and identification. This paper discusses some state-of-the-art techniques of fingerprint identification or recognition using pattern matching. In The paper we are showing classifies the recognition techniques based on ridge lines and minutiae points. All fingerprint identification using pattern matching technique can be divided into the encoding phase which is to map the fingerprint to the pattern and then matching the input pattern with the template pattern.
Performance Impact Of The User Attempts On Fingerprint Recognition System (FRS)
The Fingerprint images are used to identify the person uniquely. The special system known as Fingerprint Recognition System (FRS) is used to match Fingerprints. The overall matching and recognition should be accurate, so that it can be used in the restricted areas. It is very important to consider the performance of the Fingerprint Recognition System (FRS). Today's world has a common question i.e. 'Why Fingerprints are not commonly used?' The answer of this question is the inefficient FRS method. User identification and verification techniques are designed in such a way that only authorized users are allowed to access. So, it is require studying the functional approaches by analyzing the problems regarding verification. This research paper experimentally present the performance evaluation of Minutia Matching based FRS by using of false acceptance rate (FAR) and false rejection rate (FRR). The result of user attempts evaluates by calculating Hough Transformation.
Performance of Fingerprint Enhancement and Classification Using Neural Network
INTERNATIONAL JOURNAL OF COMPUTER APPLICATION
Fingerprints are the best and most widely used form of biometric authentication. Everyone is known to have unique, immutable fingerprints. Fingerprints are secure to use because they do not change in one"s lifetime. Since fingerprints are unique, even between identical twins, they are perfect for various security uses. The goal of biometric authentication system is to compare two fingerprint images. The authentication in aadhaar card is done by this comparison. Human fingerprints are rich in details called minutiae, which can be used as identification marks for security purposes. Fingerprint authentication systems are based on local ridge features known as minutiae extraction, marking minutiae accurately and rejecting false ones. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. To get accurate minutiae, image enhancement techniques are employed prior to minutiae extraction. This paper presents techniquesto verify the identity of fingerprint. So the minutiae points of fingerprint images are classified using support vector machine algorithm for the identification of type of fingerprints.
This paper presents a two stage novel technique for fingerprint feature extraction and classification. Fingerprint images are considered as texture patterns and Multi Layer Perceptron (MLP) is proposed as a feature extractor. The same fingerprint patterns are applied as input and output of MLP. The characteristics output is taken from single hidden layer as the properties of the fingerprints. These features are applied as an input to the classifier to classify the features into five broad classes. The preliminary experiments were conducted on small benchmark database and the found results were promising. The results were analyzed and compared with other similar existing techniques.