Biometrics and Bioinformatics

Navrit Kaur Johal ¹, Amit Kamra ¹, Kiran Jyoti ¹

Affiliations
1 Guru Nanak Dev Engineering College, Ludhiana, IN

Abstract

Singular point detection is the most important task of fingerprint image classification operation. Two types of singular points called core and delta points are claimed to be enough to classify the fingerprints. The classification can act as an important indexing mechanism for large fingerprint databases which can reduce the query time and the computational complexity. There already exists many singular point detection algorithms, Most of them can efficiently detect the core point when the image quality is fine, but when the image quality is poor, the efficiency of the algorithm degrades rapidly. In the present work, a new method of detection and localization of core points in a fingerprint image is proposed.

Keywords

Core Point, Delta Point, Orientation Field.

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Ramandeep Kaur ¹, Amit Kamra ², Vijay Dhir ³

Affiliations
1 RBIEBT, Sahuran, Punjab, IN
2 GNE, Ludhiana, IN
3 Sant Baba Bhag Singh, Hoshiarpur, IN

Abstract

Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques[1] based on high-resolution fingerprints images of the individual. Fingerprints have been used in forensic as well as commercial applications for identification as well as verification. The fingerprint surface is made up of a system of ridges and valleys. The steps for Fingerprint recognition include image acquisition, preprocessing, feature extraction and matching[2]. In the present work, a new fingerprint feature detection algorithm has been proposed. It has been found that presence of noise in fingerprint images leads to spurious minutiae. To overcome this problem, feature extraction has been done which efficiently determine the minutiae points in fingerprint [1]. The proposed method can be used in matching the template for finding bifurcation and termination. The new smoothing algorithm is proposed for the detection of the features of fingerprints. A method has been introduced for finding ridges in the fingerprint image with the help of eight different masks. It is a process of making a binary image of ridges from the grayscale fingerprint image. The experimental results showed the accuracy of the algorithm in terms of genuine acceptance rate, false rejection rate, false acceptance rate.

Keywords

Bifurcation, Orientation Field, Ridges Segmentation and Smoothing, Termination.

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