An Efficient Fingerprint Matching System for Low Quality Images

Abstract

Fingerprint-based identification is one of the most well-known and publicized biometrics for personal identification. It remains a reliable, efficient and commonly accepted biometric. In this paper, a fingerprint recognition system for identifying the low quality fingerprint images on Myanmar National Registration Cards (NRCs) is developed. Traditional minutia based approach is not robust to poor quality fingerprint images. In proposed system, ridge feature-based approach for fingerprint recognition using contextual filter and single pass thinning algorithm is developed. The input image is preprocessed and gabor filtering is applied for ridge line enhancement. The system extracts the ridge line features from the skeleton image derived from single pass thinning algorithm and it is compared to the database using Euclidean distance metric. The effectiveness of the proposed system can be confirmed through the experimental results.

Introductory Chapter: On Fingerprint Recognition

“…hybrid matching approach (minutiae-based representation with a texture-based representation) [19] Suggesting ridge feature-based approach for fingerprint recognition that provides good results for low-quality fingerprint images. Matching fingerprint images based on ridgeline features extracted by using contextual filtering and two pass thinning.…”

Wavelet Based Fingerprint Authentication System: A Review

“…Wavelet transform are used to omit down-sampling in the forward transform and up sampling in the inverse transform. Zin Mar Win [13] proposed a fingerprint recognition system based on Myanmar National Registration Cards (NRCs). Gabor filters were used to enhance the low quality images.…”

“…Figure7. Functional block diagram of pattern recognition approach [13] Texture feature of the images are used in the pattern recognition approaches for classification. The disadvantage of this approach is that it analyzes the image at one single scale.…”

Ech-a Novel Multilevel Thresholding Technique for Minutiae Based Fingerprint Recognition

“…4. Fourier transform is applied by separating the image into smaller block of images with a size of 32 by 32 pixels using the formula [9].…”

“…(white) / (0.001 + sum( white) ) * log ((white + 0.001) / (0.001 + sum (white ) ) }……..…(9) step4: Compute entropies=H B +H W step5: Find entropy threshold=max value of the entropies Output: Entropy threshold3.1.3 Algorithm for Computing Cluster ThresholdValue Input: Entropy threshold obtained from previous module step1: Let initial threshold be entropy threshold value step2: Segment the image as background and foreground according to threshold value using[11] G1 =f ( x , y ) : f ( x , y ) > T G2 =f ( x ,y ) : f ( x , y ) < T step3: Compute mean of each set i.e. m1 and m2 step4: Compute new threshold value T(i+1) = (m1+m2)/2 step5: Repeat until[T(i+1) -T ]>tol increment i by 1 step6: Find the cluster threshold value Output; Cluster threshold 3.1.4 Algorithm for Computing Histogram Based Threshold Value step1: Compute normalized histogram of input image P i = n(i)/size( m*n )………………………...(10) step2: Compute cumulative sumsP 1 (K) P1(k) = ∑ k Pi ……………………………....(11) i=0 P2(k) = ∑ L-1 Pi ……………………………..(12) i=k+1 where, Class C1[0, K] and C2[K+1, L-1] step3: Compute cumulative means m(K) m1(K) = 1 / p1(K) * ∑ k i *Pi …………….…”