Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

2017 ◽  
Vol 5 (2) ◽  
pp. 427
Author(s):  
Pakutharivu P ◽  
Srinath M. V
Keyword(s):  
Image Enhancement ◽  
Gabor Filter ◽  
Orientation Angle ◽  
Input Image ◽  
False Identification ◽  
Gabor Filtering ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Frequency Components ◽  
Fingerprint Image Enhancement

<p>Fingerprint image enhancement is the key process in IAFIS systems.  In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced.  A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image.  This paper presents an experimental summary of enhancing fingerprint images using Gabor filters.  Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, ,   and  radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features. </p>

FINGERPRINT ENHANCEMENT USING WAVELET TRANSFORM COMBINED WITH GABOR FILTER

2004 ◽  
Vol 18 (08) ◽  
pp. 1391-1406 ◽  
Author(s):  
WEIPENG ZHANG ◽  
YUAN YAN TANG ◽  
XINGE YOU
Keyword(s):  
Wavelet Transform ◽  
Gabor Filter ◽  
Wavelet Coefficient ◽  
Input Image ◽  
Identification System ◽  
Gabor Filtering ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Novel Approach

The performance of automatic fingerprint identification system (AFIS) is heavily determined by the quality of the input image, thus an effective method to enhance the fingerprint image is essential in such a system. In this paper, we combine the filter-based method, which is mostly used nowadays with wavelet transform to achieve a more reliable and effective approach to fingerprint enhancement. This novel approach consists of five main steps, namely: (1) normalization, (2) decomposition, (3) wavelet coefficient adjustment, (4) Gabor filtering, and (5) reconstruction. Using this new method, a more clear fingerprint image can be obtained, which can distinctly improve the accuracy of the minutiae extraction module and finally achieve a better performance of the entire system. Experiments have been conducted in our study and positive experimental results have been received, which show that the proposed combined method is more effective and robust than other existing methods such as the filter-based and direct gray-level approaches.

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