International Journal of Computer Applications (0975 – 8887) Volume 74– No.12, July 2013 23 Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints Neha Jain M.Tech (CSE) scholar Mewar University Gangrar, Chittorgarh S.K.Sharma,Ph.D Director and Professor Computer Engineering Pacific Institute of Engineering, Udaipur (Rajasthan) B.L.Pal Associate Professor & head Computer Science Engineering Mewar University Gangrar, chittorgarh ABSTRACT Fingerprint recognition is one of the research hotspots of biometrics techniques. Fingerprints are the most widely used biometric feature for identification and verification in the field of biometrics. [1]. The traditional fingerprint recognition systems have such disadvantages as high computation complexity, low speed, low recognition rate to uncompleted or defiled fingerprints, and not robust[2]. In this paper, we propose a multimodel fingerprint identification and verification method based on pattern recognition, which emphasizes global features of fingerprint. With lots of artificial fingerprint samples, the results show that the proposed method is effective, fast, robust and shows the Improvement in rate of false acceptance and false rejections. Experimental results are analyzed and a fingerprint recognition system is introduced. General Terms Pattern Recognition, Fingerprint classification and verification Keywords Fingerprint classification, Fingerprint identification techniques, Minutiae extraction, image enhancement 1. INTRODUCTION Biometrics means “The statistical analysis of biological observations and phenomena”. Biometric based identification relies on “something that you are”, or “something that you do”, and hence it differentiates between an authorized person and an impostor.[3] Enrollment and authentication are the two primary processes involved in a biometric security system. During enrollment, biometric measurements are captured from a subject and related information from the raw measurements is gleaned by the feature extractor, and this information is stored on the database[4]. During authentication, biometric information is detected and compared against the database through pattern recognition techniques that involve a feature extractor and a biometric matcher working in cascade[5]. Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image[6]. Digital image processing is concerned primarily with extracting useful information from images. Ideally, this is done by computers, with little or no human intervention. Image processing algorithms may be placed at three levels. At the lowest level are those techniques which deal directly with the raw, possibly noisy pixel values, with denoising and edge detection being good examples. In the middle are algorithms which utilize low level results for further means, such as segmentation and edge linking. At the highest level are those methods which attempt to extract semantic meaning from the information provided by the lower levels, for example, handwriting recognition[7]. Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration. Fingerprint recognition (sometimes referred to as dactyloscopy) or palm print identification is the process of comparing questioned and known friction skin ridge impressions from fingers or palms or even toes to determine if the impressions are from the same finger or palm. The flexibility of friction ridge skin means that no two finger or palm prints are ever exactly alike (never identical in every detail), even two impressions recorded immediately after each other[8]. Fingerprint identification (also referred to as individualization) occurs when an expert(or an expert computer system operating under threshold scoring rules) determines that two friction ridge impressions originated from the same finger or palm (or toe, sole) to the exclusion of all others[9]. The accuracy of a fingerprint matching algorithm is measured by: FAR : It stands for false acceptance rate. It is defined as the ratio of the number of impostor images considered as authentic by the algorithm to the total number of impostor images. FRR : It stands for false rejection rate. It is defined as the ratio of the number of authentic images not considered qualified by the algorithm to the total number of authentic images. When FAR and FRR are equal, we call it equal error
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International Journal of Computer Applications (0975 – 8887)
Volume 74– No.12, July 2013
23
Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints
Neha Jain M.Tech (CSE) scholar
Mewar University Gangrar, Chittorgarh
S.K.Sharma,Ph.D Director and Professor Computer Engineering
Pacific Institute of Engineering, Udaipur (Rajasthan)
B.L.Pal Associate Professor & head
Computer Science Engineering Mewar University
Gangrar, chittorgarh
ABSTRACT
Fingerprint recognition is one of the research hotspots of
biometrics techniques. Fingerprints are the most widely used
biometric feature for identification and verification in the field
of biometrics. [1]. The traditional fingerprint recognition
systems have such disadvantages as high computation
complexity, low speed, low recognition rate to uncompleted
or defiled fingerprints, and not robust[2]. In this paper, we
propose a multimodel fingerprint identification and
verification method based on pattern recognition, which
emphasizes global features of fingerprint. With lots of
artificial fingerprint samples, the results show that the
proposed method is effective, fast, robust and shows the
Improvement in rate of false acceptance and false rejections.
Experimental results are analyzed and a fingerprint
recognition system is introduced.
General Terms
Pattern Recognition, Fingerprint classification and verification