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This paper details the work of an efficient fingerprint matching technique via the use of wavelet based features. It is a kind of image based processing technique. So first core point is detected via using the hybrid technique. Then wavelet is applied on a smaller region cropped around the core point. The proposed system uses three types of feature...
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This paper deals with the design and development of an Electronic Voting System (EVS) Using Fingerprint Recognition. The proposed EVS allows the voter to scan their fingerprint, which is then matched with an already saved image within a database, using Matlab with the help of Gabor filter method. Gabor filter method shows improved result as compare...
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... The image is cropped to the size of 128 × 128 pixels using its core point as the center. Huang and Aviyente [14] and Khan and Javed [15] employed wavelet based analysis in order to provide rich discriminatory texture structure for fingerprint verification. To capture global transformation between two fingerprint images, genetic algorithm is adopted by Tan and Bhannu [16]. ...
In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastically structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.
... The image is cropped to the size of 128 × 128 pixels using its core point as the center. Huang and Aviyente [14] and Khan and Javed [15] employed wavelet based analysis in order to provide rich discriminatory texture structure for fingerprint verification. To capture global transformation between two fingerprint images, genetic algorithm is adopted by Tan and Bhannu [16]. ...
Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition from large database is an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. For that we need effective and efficient fingerprint matching algorithms that meet user requirements, to identify similarity. This paper gives a brief survey of current fingerprint matching methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of fingerprint matching, image feature representation and extraction. Furthermore future research directions are suggested.
A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.
