Efficient 2‐fold contextual filtering approach for fingerprint enhancement

Ghafoor, Mubeen; Taj, Imtiaz A.; Waqas, Ahmad; Jafri, Noman

doi:10.1049/iet-ipr.2013.0528

Cited by 23 publications

(23 citation statements)

References 19 publications

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“…The second stage comprises a frequency bandpass filter which is separable in angular and radial frequency domain and its parameters are learned from the input images and first stage enhanced images. In [16], Ghafoor et. al proposed a method based on 2-fold local adaptive contextual filtering.…”

Section: Both Spatial and Frequency Domain Enhancement Methodsmentioning

confidence: 99%

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Sharma

Dey

2019

Image and Vision Computing

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Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, normal wet and good quality using fuzzy c-means technique. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity and ridge valley uniformity into account for clustering the fingerprint images in appropriate quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement process. A quality adaptive preprocessing method is used as front-end before enhancing the fingerprint images with Gabor, short term Fourier transform and oriented diffusion filtering based enhancement techniques. Experimental results show improvement in the verification results for FVC2004 datasets. Significant improvement in equal error rate is observed while using quality adaptive preprocessing based approaches in comparison to the current stateof-the-art enhancement techniques.

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Section: Both Spatial and Frequency Domain Enhancement Methodsmentioning

confidence: 99%

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Sharma

Dey

2019

Image and Vision Computing

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“…The McClellan transform [ 33 – 36 ], would be one favorable method. Also, recently, bandpass filtering with a radial filter in the Fourier domain has been proposed by [ 37 ], [ 38 ] and [ 39 ] et al for enhancing fingerprint images. However, for a simpler reconstruction of 2D filter and a data-friendly alternative to the polar tiling of the frequency plane, a Cartesian array is used instead (see [ 28 ], [ 29 ], [ 40 ], [ 41 ]).…”

Section: Fingerprint Segmentation By Fdb Methodsmentioning

confidence: 99%

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Thai¹,

Huckemann²,

Gottschlich³

2016

PLoS ONE

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Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

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“…The most widely accepted technique for image enhancement that reduces the effect of broken ridges and unwanted scars on the fingerprint is contextual filtering in spatial and frequency domains [8, 9]. Sherlock et al [10] proposed filter‐bank based fingerprint enhancement scheme as a frequency domain form of contextual filters.…”

Section: Contextual Filtering‐based Fingerprint Enhancementmentioning

confidence: 99%

Fingerprint frequency normalisation and enhancement using two‐dimensional short‐time Fourier transform analysis

Ghafoor

Taj

Jafri

2016

IET Computer Vision

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A fingerprint image with non‐uniform ridge frequencies can be considered as a two‐dimensional dynamic signal. A non‐uniform stress on the sensing area applied during fingerprint acquisition may result in a non‐linear distortion that disturbs the local frequency of ridges adversely affecting the matching performance. This study presents a new approach based on Short time Fourier transform analysis and local adaptive contextual filtering for frequency distortion removal and enhancement. In the proposed approach, the fingerprint image is divided into sub‐images to determine local dominant frequency and orientation. Gaussian Directional band pass filtering is then adaptively applied in frequency domain. These filtered sub‐images are then combined in spatial domain using a novel technique to obtain the enhanced fingerprint image of high ridge quality and uniform inter‐ridge distance. Simulation results show the efficacy of the proposed enhancement technique as compared to other well‐known contextual filtering based enhancement techniques reported in the literature.

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Efficient 2‐fold contextual filtering approach for fingerprint enhancement

Cited by 23 publications

References 19 publications

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Fingerprint frequency normalisation and enhancement using two‐dimensional short‐time Fourier transform analysis

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