Quality Induced Fingerprint Identification using Extended Feature Set
08 Dec 2008-pp 1-6
TL;DR: Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the novel algorithm presented offers significant benefits for fast fingerprint identification.
Abstract: Automatic fingerprint identification systems use level-1 and level-2 features for fingerprint identification. However, forensic examiners utilize inherent level-3 details along with level-2 features. Existing level-3 feature extraction algorithms are computationally expensive to be used for identification. This paper presents a novel algorithm for fast level-3 feature extraction and identification. The algorithm starts with computing local image quality score using redundant discrete wavelet transform. A fast curve evolution algorithm is then used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Along with level-1 and level-2 features, these level-3 features are used in a Delaunay triangulation based indexing algorithm. Finally, quality-based likelihood ratio is used to further improve the identification performance. Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the algorithm offers significant benefits for fast fingerprint identification.
Citations
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TL;DR: This paper proposes the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images, and uses specifically designed and trained Convolutional Neural Networks to estimate and refine the centroid of each pore.
81 citations
Cites background from "Quality Induced Fingerprint Identif..."
...There also quality assessment methods that analyze porebased features [44, 47] and image reconstruction methods based on the pores extracted from fingerprint images presenting low contrast between the ridges and valleys [41]....
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...Another important application that can use pore characteristics is the quality assessment of fingerprint samples [47]....
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TL;DR: The process of automatic latent fingerprint matching is divided into five definite stages, and the existing algorithms, limitations, and future research directions in each of the stages are discussed.
Abstract: Latent fingerprint has been used as evidence in the court of law for over 100 years. However, even today, a completely automated latent fingerprint system has not been achieved. Researchers have identified several important challenges in latent fingerprint recognition: 1) low information content; 2) presence of background noise and nonlinear ridge distortion; 3) need for an established scientific procedure for matching latent fingerprints; and 4) lack of publicly available latent fingerprint databases. The process of automatic latent fingerprint matching is divided into five definite stages, and this paper discusses the existing algorithms, limitations, and future research directions in each of the stages.
72 citations
Cites methods from "Quality Induced Fingerprint Identif..."
...[92] proposed a method to combine pore and ridge features with minutiae for improved verification....
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TL;DR: The design of a sequential fusion technique that uses the likelihood ratio test-statistic in conjunction with a support vector machine classifier to account for errors in the former and a dynamic selection algorithm that unifies the constituent classifiers and fusion schemes in order to optimize both verification accuracy and computational cost is proposed.
Abstract: Biometric fusion consolidates the output of multiple biometric classifiers to render a decision about the identity of an individual. We consider the problem of designing a fusion scheme when 1) the number of training samples is limited, thereby affecting the use of a purely density-based scheme and the likelihood ratio test statistic; 2) the output of multiple matchers yields conflicting results; and 3) the use of a single fusion rule may not be practical due to the diversity of scenarios encountered in the probe dataset. To address these issues, a dynamic reconciliation scheme for fusion rule selection is proposed. In this regard, the contribution of this paper is two-fold: 1) the design of a sequential fusion technique that uses the likelihood ratio test-statistic in conjunction with a support vector machine classifier to account for errors in the former; and 2) the design of a dynamic selection algorithm that unifies the constituent classifiers and fusion schemes in order to optimize both verification accuracy and computational cost. The case study in multiclassifier face recognition suggests that the proposed algorithm can address the issues listed above. Indeed, it is observed that the proposed method performs well even in the presence of confounding covariate factors thereby indicating its potential for large-scale face recognition.
58 citations
Cites methods from "Quality Induced Fingerprint Identif..."
...• To encode the facial edge information and noise present in the image, a redundant discrete wavelet transformation (RDWT) based quality assessment algorithm [25] is used that provides both frequency and spatial information....
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TL;DR: A novel descriptor based minutiae detection algorithm for latent fingerprints that shows promising results on latent fingerprint matching on the NIST SD-27 database.
Abstract: Latent fingerprint identification is of critical importance in criminal investigation. FBI’s Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated latent fingerprint identification is limited due to imprecise automated feature (minutiae) extraction, specifically due to noisy ridge pattern and presence of background noise. In this paper, we propose a novel descriptor based minutiae detection algorithm for latent fingerprints. Minutia and non-minutia descriptors are learnt from a large number of tenprint fingerprint patches using stacked denoising sparse autoencoders. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia patch. Experiments performed on the NIST SD-27 database shows promising results on latent fingerprint matching.
50 citations
Cites methods from "Quality Induced Fingerprint Identif..."
...Paulino et al. [16] used MCC to describe manually annotated minutia neighbourhood....
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TL;DR: An extensive review of the work done by eminent researchers in the development of an automated latent fingerprint identification system is provided.
38 citations
References
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TL;DR: This work presents a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature and is based on a two-stage classifier to make a classification.
Abstract: Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.
639 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...1 features or fingerprint patterns are extracted using the multi-channel approach [12] and are categorized into five classes: arch, tented arch, right loop, left loop, and whorl....
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TL;DR: Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
Abstract: Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
538 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...We next propose the use of quality-based likelihood ratio [15] to attune the top M matches....
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TL;DR: The technique of scale multiplication is analyzed in the framework of Canny edge detection and the detection and localization criteria of the scale multiplication are derived, finding that at a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication.
Abstract: The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.
515 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...Step 1: Scale multiplication based edge detection algorithm (that multiplies filter response at adjacent scales to enhance the edge structure and detects the edges as the local maxima) [1] is applied on the input fingerprint image....
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...To address this issue, we propose a scheme that utilizes the scale multiplication based Canny edge detection [1] for finding the initial b and then applies a two-cycle fast curve evolution algorithm with smoothness regularization [19]....
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TL;DR: A hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans, including pores and ridge contours, is proposed, which shows that level 3 features carry significant discriminatory information.
Abstract: Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, level 1 (pattern), level 2 (minutia points), and level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of level 3 features to assist in identification, automated fingerprint identification systems (AFIS) currently rely only on level 1 and level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the iterative closest point (ICP) algorithm. Our experiments show that level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when level 3 features are employed in combination with level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images
369 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...Researchers have proposed algorithms for level-3 feature based fingerprint verification (1:1 matching) [6], [10], [20]....
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TL;DR: In this letter, a precise relationship between RDWT-domain and original-signal-domain distortion for additive white noise in the RDWT domain is derived.
Abstract: The behavior under additive noise of the redundant discrete wavelet transform (RDWT), which is a frame expansion that is essentially an undecimated discrete wavelet transform, is studied. Known prior results in the form of inequalities bound distortion energy in the original signal domain from additive noise in frame-expansion coefficients. In this letter, a precise relationship between RDWT-domain and original-signal-domain distortion for additive white noise in the RDWT domain is derived.
331 citations
"Quality Induced Fingerprint Identif..." refers background in this paper
...Multilevel RDWT decomposition provides the persubband noise relationship and the spatial and frequency information which are helpful in computing the edge and noise information [8]....
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