Development of Image Enhancement and the Feature Extraction Techniques on Rural Fingerprint Images to Improve the Recognition and the Authentication Rate.
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.
Citations
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01 Apr 2018
TL;DR: A new fingerprint classification method based on modified Histograms of Oriented Gradients (HOG) descriptor is proposed, which achieved the average accuracy of 98.70, which is better than those of the state-of-the-art fingerprint classification methods.
Abstract: The processing time during fingerprint recognition is a main problem when the fingerprint database is huge. Classifying fingerprints into subcategories is an effective way to restrict the search space into a sub-database. We propose a new fingerprint classification method based on modified Histograms of Oriented Gradients (HOG) descriptor. The way orientation field is computed in HOG descriptor is not adapted to the ridge patterns. We compute the orientation field, which is adapted to the ridge patterns and incorporate in HOG descriptor, enhancing its potential to represent a fingerprint in a robust way. Extreme Learning Machine (ELM) with RBF kernel is used as a classifier. We performed experiments on the noisy fingerprint database FVC-2004, a benchmark database; the proposed method achieved the average accuracy of 98.70, which is better than those of the state-of-the-art fingerprint classification methods.
9 citations
Cites background from "Development of Image Enhancement an..."
...Ridge orientation filed plays a key role in fingerprint recognition [6]....
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TL;DR: A technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification is proposed, which automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between- class scatter to within-class scatter.
Abstract: Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.
7 citations
TL;DR: Analysis of the obtained results revealed that for reliable and optimal performance of fingerprint matching systems, false minutiae points must be eliminated as much as possible from their operations.
Abstract: This paper presents a report on the experimental study of the impact of false minutiae on the performance of fingerprint matching systems. A 3-tier algorithm comprising of preprocessing, minutiae extraction and post-processing stages formed the backbone of the experiments. The pre-processing stage enhanced the fingerprint image, the minutiae extraction stage used the minutiae properties to detect and extract true and false minutiae points while the post-processing stage eliminated the false minutiae points. The experiments were performed on the four datasets in each of the three standard fingerprint databases; namely FVC2000, FVC2002 and FVC2004. The completion times for the minutiae extraction and the post-processing algorithms on each dataset were measured. A standard fingerprint matching algorithm was also implemented for verifying the impact of false minutiae points on FAR, FRR and the matching speed. Analysis of the obtained results revealed that for reliable and optimal performance of fingerprint matching systems, false minutiae points must be eliminated as much as possible from their operations.
3 citations
Cites background from "Development of Image Enhancement an..."
...Commonly used minutiae are the end points (enclosed in circles) and bifurcations (enclosed in square) in Figure 2 [15-19]....
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...The implementation of very safe and reliable fingerprint minutiae extraction strategies is therefore important for ensuring accuracy [16-18]....
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Dissertation•
14 Aug 2015
TL;DR: In this paper, the authors propose a novel approach to solve the problem of homonymity in homonymization, i.e., homonymonymity-of-homonymity.
Abstract: viii CHAPTER
1 citations
Cites background from "Development of Image Enhancement an..."
...It can be somewhat said, that their self-conscious nature leads them to work tremendously hard in that direction [60] ....
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...Hence, they are somewhat influential [60] ....
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...People with number of whorls have also been found to be good orators [60] ....
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...Overly ambitious, competitive and hardworking are some of the positive traits that a person with elongated whorl carries with himself regarding the particular intelligence associated with the finger on which this print is present [60] ....
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01 Sep 2014
TL;DR: This paper has used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1632 fingerprints images and pre-preprocessed 1632 sample images using De-noising techniques use for image enhancement and tried to improve the quality of images.
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 we have used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1632 fingerprints images. Out of which we have pre-preprocessed 1632 sample images using De-noising techniques use for image enhancement and tried to improve the quality of images. The resultant images quality is verified by using different enhancement, it is found that quality has been improved. Hence it is proved that the recognition rate is increases.
1 citations
References
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Book•
01 Dec 2003
TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.
6,306 citations
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
TL;DR: A fast fingerprint enhancement algorithm is presented, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.
Abstract: In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
2,212 citations
TL;DR: This work proposes an original technique, based on ridge line following, where the minutiae are extracted directly from gray scale images, and results achieved are compared with those obtained through some methods based on image binarization.
Abstract: Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. Automatic minutiae detection is an extremely critical process, especially in low-quality fingerprints where noise and contrast deficiency can originate pixel configurations similar to minutiae or hide real minutiae. Several approaches have been proposed in the literature; although rather different from each other, all these methods transform fingerprint images into binary images. In this work we propose an original technique, based on ridge line following, where the minutiae are extracted directly from gray scale images. The results achieved are compared with those obtained through some methods based on image binarization. In spite of a greater conceptual complexity, the method proposed performs better both in terms of efficiency and robustness.
677 citations
TL;DR: This work presents an approach that uses localized secondary features derived from relative minutiae information that is directly applicable to existing databases and balances the tradeoffs between maximizing the number of matches and minimizing total feature distance between query and reference fingerprints.
Abstract: Matching incomplete or partial fingerprints continues to be an important challenge today, despite the advances made in fingerprint identification techniques. While the introduction of compact silicon chip-based sensors that capture only part of the fingerprint has made this problem important from a commercial perspective, there is also considerable interest in processing partial and latent fingerprints obtained at crime scenes. When the partial print does not include structures such as core and delta, common matching methods based on alignment of singular structures fail. We present an approach that uses localized secondary features derived from relative minutiae information. A flow network-based matching technique is introduced to obtain one-to-one correspondence of secondary features. Our method balances the tradeoffs between maximizing the number of matches and minimizing total feature distance between query and reference fingerprints. A two-hidden-layer fully connected neural network is trained to generate the final similarity score based on minutiae matched in the overlapping areas. Since the minutia-based fingerprint representation is an ANSI-NIST standard [American National Standards Institute, New York, 1993], our approach has the advantage of being directly applicable to existing databases. We present results of testing on FVC2002's DB1 and DB2 databases.
261 citations