Adaptive Fingerprint Image Enhancement Based on Spatial Contextual Filtering and Preprocessing of Data

Abstract
Authors
Keywords
Conclusion
References

Although fingerprint recognition technology has advanced rapidly there are still some challenging research problems. Research has been conducted to develop Automatic Fingerprint Identification Systems (AFIS). The main problem in automatic fingerprint identification is to acquire matching reliable features from poor quality fingerprint images. Another challenging problem is the processing and matching of overlapped fingerprints. So, this paper proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term “adaptive” implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. There are situations where several fingerprints overlap on top of each other. A future enhancement is to separate such overlapped fingerprints into component fingerprints and enhance them so that existing fingerprint matchers can recognize them. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.

Published In : IJCAT Journal Volume 1, Issue 4

Date of Publication : 31 May 2014

Pages : 56 - 65

Figures : 04

Tables : --

Publication Link : Adaptive Fingerprint Image Enhancement Based on Spatial Contextual Filtering and Preprocessing of Data

Divya. V. : Electronics & Communication Engineering Department, VTU University, SDIT, Mangalore, Karnataka, INDIA.

Directional filtering

Fourier transform

image processing

spectral feature estimation

successive mean quantization transform

This paper presents an adaptive fingerprint enhancement method. The method extends previous work by focusing on preprocessing of data on a global and a local level. A preprocessing using the non-linear SMQT dynamic range adjustment method is used to enhance the global contrast of the fingerprint image prior to further processing. Estimation of the fundamental frequency of the fingerprint image is improved in the global analysis by utilizing a median filter leading to a robust estimation of the local area size. A low-order SMQT dynamic range adjustment is conducted locally in order to achieve reliable features extraction used in the matched filter design and in the image segmentation. The matched filter block is improved by applying order statistical filtering to the extracted features, thus reducing spurious outliers in the feature data. The proposed method combines and updates existing processing blocks into a new and robust fingerprint enhancement system. The updated processing blocks lead to a drastically increased method performance. The proposed method improves the performance in relation to the NIST method, and this is particularly pronounced on fingerprint images having a low image quality. The proposed algorithm is insensitive to the varying characteristics of fingerprint images obtained by different sensors.

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