A Novel and Efficient Algorithm for Segmentation of Fingerprint Image Based on LBP Operator

Wen, Chengming; Guo, Tiande; Zhou, Yuyang

doi:10.1109/itcs.2009.180

Cited by 7 publications

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is an extension of the Local Binary Pattern (LBP) [37] texture operator. The CLBP operator gives both sign CLBP and magnitude components CLBP for each pixel from its neighbouring pixels.…”

Section: Complete Local Binary Pattern (Clbp)mentioning

confidence: 99%

An Efficient Reconfigurable Architecture for Fingerprint Recognition

2016

View full text Add to dashboard Buy / Rent full text

The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.

show abstract

Section: Complete Local Binary Pattern (Clbp)mentioning

confidence: 99%

An Efficient Reconfigurable Architecture for Fingerprint Recognition

2016

View full text Add to dashboard Buy / Rent full text

show abstract

“…This idea is related to the use of the average gradient magnitude and variance of the gradient orientation proposed by Qi and Mei. 36 Other features proposed in the literature include the use of the local binary pattern (LBP) operator, 45,48 and complementary variance energy. 19 Intuitively, the features proposed for¯ngerprint segmentation are conceptually similar in the sense that they attempt to quantify the observation that¯ngerprints are formed by a succession of alternating ridge/valleys with approximately constant orientation.…”

Section: Background and Previous Workmentioning

confidence: 99%

Fingerprint Image Segmentation Using Data Manifold Characteristic Features

Paiva

Tasdizen

2012

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Buy / Rent full text

Automatic¯ngerprint identi¯cation systems (AFIS) have been studied extensively and are widely used for biometric identi¯cation. Given its importance, many well-engineered methods have been developed for the di®erent stages that encompass those systems. The¯rst stage of any such system is the segmentation of the actual¯ngerprint region from the background. This is typically achieved by classifying pixels, or blocks of pixels, based on a set of features. In this paper, we describe novel features for¯ngerprint segmentation that express the underlying manifold topology associated with image patches in a local neighborhood. It is shown that ngerprint patches seen in a high-dimensional space form a simple and highly regular circular manifold. The characterization of the manifold topology suggests a set of optimal features that characterize the local properties of the¯ngerprint. Thus,¯ngerprint segmentation can be formulated as a classi¯cation problem based on the deviation from the expected topology. This leads to features that are more robust to changes in contrast than mean, variance and coherence. The superior performance of the proposed features for¯ngerprint segmentation is shown in the eight datasets from the 2002 and 2004 Fingerprint Veri¯cation Competitions.

show abstract

“…The most common features include local/global mean gray value, standard deviance, coherence, clutter degree, LBP, energy, Gabor response, and features derived from the mentioned, etc [4] [5] [6] [7]. Although approaches using combined mentioned features and unsupervised or supervised classifiers generally improve the segmentation accuracy of the low quality fingerprint [8], the process is much complicated and timeconsuming especially the training stage for the supervised case, and moreover, in practice the class is highly possibly insufficiently sampled, like samples of remaining fingerprints that left by previous sampling, and may only take effective in the case of fingerprint images that captured at similar conditions.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of fingerprint image by using polarimetric feature

Chen

Zhang

Zhao

2010

2010 International Conference on Autonomous and Intelligent Systems, AIS 2010

View full text Add to dashboard Buy / Rent full text

Segmentation of fingerprint image is the first step of fingerprint recognition, and it plays an essential role which helps to preserve genuine and reduce false minutiae and further aids the performance of Automatic Fingerprint Identification System (AFIS). The problem of segmentation has been thoroughly studied but never been completely solved. During this paper, we propose a novel representation of fingerprint image with the added polarimetric information which is captured by Stokes Imaging System, and followed by a simple yet efficient segmentation of fingerprint image based on the polarimetric variance (Polvar). Polarimetric characteristic is another distinguishable feature beside intense that reflecting light carries, and it provides potential way to enhance the contrast between background and foreground, and between ridges and valleys as well. And therefore, there is a possibility to achieve a satisfactory segmentation results. Nonoverlapping block Polvar feature is utilized to accelerate computation, and moreover the segmentation results that are based on other common used features are compared, that is block energy, block coherence, block cluster degree. Experimental results show that our proposed novel method is much efficient than the other features and simultaneously achieve higher accuracy especially it well segments the case of remaining ridges from previously scanned finger. Segmentation results are evaluated both visually by human inspire and quantitatively.

show abstract

A Novel and Efficient Algorithm for Segmentation of Fingerprint Image Based on LBP Operator

Cited by 7 publications

References 6 publications

An Efficient Reconfigurable Architecture for Fingerprint Recognition

An Efficient Reconfigurable Architecture for Fingerprint Recognition

Fingerprint Image Segmentation Using Data Manifold Characteristic Features

Segmentation of fingerprint image by using polarimetric feature

Contact Info

Product

Resources

About