On analysis of rural and urban indian fingerprint images
04 Jan 2010-pp 55-61
TL;DR: The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance and fingerprint recognition algorithms provide comparatively better performance on urban population.
Abstract: This paper presents a feasibility study to compare the performance of fingerprint recognition on rural and urban Indian population. The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance. On the other hand, fingerprint recognition algorithms provide comparatively better performance on urban population. The study also shows that poor images quality, worn and damaged patterns and some special characteristics affect the performance of fingerprint recognition.
Citations
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01 Jan 2013
TL;DR: The rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images is used and preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint image and tried to improve the quality of images.
Abstract: Fingerprint recognition is one of the most popular and successful methods used for person identification which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae which are points where a extracts the ridges and bifurcation from a fingerprint image. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality. Fingerprint image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations. Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various distortions introduced during the acquisition process. In this paper we have used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images. Out of which we have preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint image and tried to improve the quality of images. The Resultant images quality is verified by using different quality measures.
6 citations
TL;DR: In this paper , a fingerprint restoration model for a poor quality fingerprint that reconstructs a binarized fingerprint image with an improved ridge structure was presented, where a lightweight attention mechanism was proposed to perform channel refinement by reducing redundancy among channel weights of the convolutional kernels.
Abstract: The state-of-the-art fingerprint matching systems achieve high accuracy on good quality fingerprints. However, degraded fingerprints obtained due to poor skin conditions of subjects or fingerprints obtained around a crime scene often have noisy background and poor ridge structure. Such degraded fingerprints pose problem for the existing fingerprint recognition systems. This paper presents a fingerprint restoration model for a poor quality fingerprint that reconstructs a binarized fingerprint image with an improved ridge structure. In particular, we demonstrate the effectiveness of channel refinement in fingerprint restoration. The state-of-the-art channel refinement mechanisms, such as Squeeze and Excitation (SE) block, in general, create SE- block introduce redundancy among channel weights and degrade the performance of fingerprint enhancement models. We present a lightweight attention mechanism that performs channel refinement by reducing redundancy among channel weights of the convolutional kernels. Restored fingerprints generated after introducing proposed channel refinement unit obtain improved quality scores on standard fingerprint quality assessment tool. Furthermore, restored fingerprints achieve improved fingerprint matching performance. We also illustrate that the idea of introducing a channel refinement unit is generalizable to different deep architectures. Additionally, to quantify the ridge preservation ability of the model, standard metrics: Dice score, Jaccard Similarity, SSIM and PSNR are computed with the ground truth and the output of the model (CR-GAN). An ablation study is conducted to individually quantify the improvement of generator and discriminator sub-networks of CR-GAN through channel refinement. Experiments on the publicly available IIITD- MOLF, Rural Indian Fingerprint Database and a private rural fingerprint database demonstrate the efficacy of the proposed attention mechanism.
3 citations
TL;DR: Results indicate that the proposed CDC-GAN outperforms state-of-the-art fingerprint denoising algorithms on challenging publicly available poor-quality fingerprint databases.
Abstract: Performance of the state-of-the-art fingerprint denoising model on poor-quality fingerprints degrades due to cross-domain shift observed between training and testing domains. To address this limitation, we present a cross-domain consistent fingerprint denoising model, which ensures that the output of two fingerprint images with the same ridge structure, however, varying contrast and ridge-valley clarity should be similar. Results indicate that the proposed CDC-GAN outperforms state-of-the-art fingerprint denoising algorithms on challenging publicly available poor-quality fingerprint databases.
3 citations
TL;DR: In this paper the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1632 fingerprints images is used and it is proved that the recognition rate is increases.
Abstract: Identification and authentication is done using various biometric sign like fingerprints. The recognition rate of correct person is depending on quality of fingerprints images. Fingerprints quality also varying from rural and urban population. Rural population having more physical work than urban population. Therefore the ridges, valleys, bifurcation, joints, minutia etc. features are not good quality hence it reduces recognition rate accuracy. To improve recognition rate of such images there is strong need to first improve the quality of features. In this paper used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1632 fingerprints images. Out of which preprocess 100 sample images using histogram equalization and tried to improve the quality of images. The resultant images quality is verified by using different quality measures like PSNR, MSE, MAXERR, L2RAT, it is found that quality has been improved. Hence it is proved that the recognition rate is increases.
2 citations
TL;DR: A palm-based biometric identification system on mobile devices was found to be an easy-to-use and accurate technology for the unique identification of individuals compared to an existing name-based application.
Abstract: ABSTRACT Background Unique identifiers are not universal in low- and middle-income countries. Biometric solutions have the potential to augment existing name-based searches used for identification in these settings. This paper describes a comparison of the searching accuracy of a palm-based biometric solution with a name-based database. Objective To compare the identification of individuals between a palm-based biometric solution to a name-based District Health Information Software 2 (DHIS2) Android application, in a low-resource setting. Methods The study was conducted in Chandpur district, Bangladesh. Trained data collectors enrolled 150 women of reproductive age into two android applications – i) a name-based DHIS2 application, and ii) a palm-based biometric solution – both run on tablets. One week after enrollment, a different research team member attempted to re-identify each enrolled woman using both systems. A single image or text-based name was used for searching at the time of re-identification. We interviewed data collectors at the end of the study. Results Significantly more women were successfully identified on the first attempt with a palm-based biometric application (84%) compared with the name-based DHIS2 application (61%). The proportion of identifications that required three or more attempts was similar between name-based (7%, CI 3.7–12.3) and palm-based biometric system (5%, CI: 1.9–9.4). However, the total number of attempts needed was significantly lower with the palm-based solution (mean 1.2 vs. 1.5, p < 0.001). In a group discussion, data collectors reported that the palm-based biometric identification system was both accurate and easy to use. Conclusion A palm-based biometric identification system on mobile devices was found to be an easy-to-use and accurate technology for the unique identification of individuals compared to an existing name-based application. Our findings imply that palm-based biometrics on mobile devices may be the next step in establishing unique identifiers in remote and rural settings where they are currently absent.
1 citations
References
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Book•
10 Mar 2005TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators
3,821 citations
"On analysis of rural and urban indi..." refers background in this paper
...Fingerprints are considered reliable to identify individuals and are used in both biometric and forensic applications [1]....
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TL;DR: An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner and an alignment-based elastic matching algorithm has been developed.
Abstract: Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.
1,376 citations
TL;DR: A filter-based fingerprint matching algorithm which uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode and is able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature.
Abstract: Biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
1,207 citations
"On analysis of rural and urban indi..." refers methods in this paper
...– Implementation of minutiae feature extraction and matching [2] termed as Algorithm - A – Implementation of filterbank based feature extraction and matching [3] termed as Algorithm - B – Two commercial fingerprint recognition systems, termed as System - C and System - D(1)...
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TL;DR: In this article, a filter bank based fingerprint representation is implemented and the overall performance of the developed system is tested and the results have shown that this system can be used effectively for secure online verification applications.
Abstract: As organizations search for more secure authentication methods for user access, e-commerce, and other security applications, biometrics is gaining increasing attention. With an increasing emphasis on the emerging automatic personal identification applications, fingerprint based identification is becoming more popular. The most widely used fingerprint representation is the minutiae based representation. The main drawback with this representation is that it does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Also, it is difficult quickly to match two fingerprint images containing different number of unregistered minutiae points. In this study filter bank based representation, which eliminates these weakness, is implemented and the overall performance of the developed system is tested. The results have shown that this system can be used effectively for secure online verification applications.
69 citations
TL;DR: A novel online fingerprint verification algorithm and distribution system that is insensitive to fingerprint image distortion, scale, and rotation, and robust even on poor quality fingerprint images is proposed.
Abstract: In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastic structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.
7 citations
"On analysis of rural and urban indi..." refers methods in this paper
...– Implementation of minutiae feature extraction and matching [2] termed as Algorithm - A – Implementation of filterbank based feature extraction and matching [3] termed as Algorithm - B – Two commercial fingerprint recognition systems, termed as System - C and System - D(1)...
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