Fingerprint feature extraction and classification by learning the characteristics of fingerprint patterns
01 Jan 2011-Neural Network World (Czech Technical University in Prague - Central Library)-Vol. 21, Iss: 3, pp 219-226
TL;DR: This paper presents a two stage novel technique for fingerprint feature extraction and classification using Multi Layer Perceptron (MLP) as a feature extractor.
Abstract: This paper presents a two stage novel technique for fingerprint feature extraction and classification. Fingerprint images are considered as texture patterns and Multi Layer Perceptron (MLP) is proposed as a feature extractor. The same fingerprint patterns are applied as input and output of MLP. The characteristics output is taken from single hidden layer as the properties of the fingerprints. These features are applied as an input to the classifier to classify the features into five broad classes. The preliminary experiments were conducted on small benchmark database and the found results were promising. The results were analyzed and compared with other similar existing techniques.
Citations
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TL;DR: In this article, the authors proposed an approach to fingerprint classification using convolutional neural networks, which avoided the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier.
Abstract: Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.
58 citations
Cites background or methods from "Fingerprint feature extraction and ..."
...In [22], two single hidden layer perceptrons are used to classify the fingerprints into the five usual classes....
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...Some authors have published proposals in this direction, such as a succession of an autoencoder and a neural network classifier [22] or a succession of 1-layer autoencoders followed by a DNN that performs the classification [23]....
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Posted Content•
TL;DR: In this paper, the authors proposed an approach to fingerprint classification using convolutional neural networks, which avoided the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier.
Abstract: Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.
49 citations
TL;DR: This paper has proposed a robust framework to detect spoofing attacks in fingerprint recognition, which involves contrast enhancement using histogram equalization and a deep convolutional neural network architecture.
Abstract: Online banking and financial services using mobile applications are seeing a persistent growth among customers, who are using these for their financial transactions. This rise in the use of such applications in smart devices has increased security concerns. There is need for secure mechanisms to prevent fraud and protect personal information. This paper investigates the use of biometric identification in banking and financial services, which leverage the use of smartphones and tablets. While customer engagement and brand loyalty are important concerns, these services are making use of biometric authentication to make customer interactions more secure. However, as technology is growing rapidly, spoofing attacks are becoming common. In this paper, authors have proposed a robust framework to detect spoofing attacks in fingerprint recognition. The process of spoofing detection involves contrast enhancement using histogram equalization and a deep convolutional neural network architecture. Authors have validated the results on various biometric spoofing benchmarks, each one containing real and spoofed samples of user fingerprints. The results indicate that our proposed framework performs better as evaluated against other existing pre-trained CNN models and state-of-the-art methods.
19 citations
TL;DR: The goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints, and to investigate possibilities of incorporating artificial neural net- works into fingerprint recognition process and implement and document the system.
Abstract: Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable re- sults. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demon- strated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural net- works into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose im- pact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sen- sor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fin- gerprint matching module using the industry standard BOZORTH-3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw con- clusions about a very positive impact of neural network on overall recognition rate, specifically in low quality.
19 citations
TL;DR: The primary goal of this study is to improve the efficacy of the fingerprint classification by dealing with false positives by employing Bayesian model uncertainty.
Abstract: Fingerprint classification is vital for reducing the search time and computational complexity of the fingerprint identification system. The robustness of classifier relies on the strength of extracted features and the ability to deal with low-quality fingerprints. The proficiency to learn accurate features from raw fingerprint images rather than explicit feature extraction makes deep convolutional neural networks (DCNNs) attractive for fingerprint classification. The DCNNs use softmax for quantifying model confidence of a class for an input fingerprint image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with the available training examples. The primary goal of this study is to improve the efficacy of the fingerprint classification by dealing with false positives by employing Bayesian model uncertainty. The efficacy of the proposed method is shown through experimentations on NIST special database 4 (NIST-4) and fingerprint verification competition 2002 database 1-A (FVC DB1-A) 2002 and 2004 datasets. Results show that 0.8-1.0% of accuracy is improved with model uncertainty over the conventional DCNN.
17 citations
References
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TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
15,696 citations
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
4,678 citations
"Fingerprint feature extraction and ..." refers background in this paper
...Biometric cannot be borrowed, stolen or forgotten [1]....
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TL;DR: An overview of biometrics is provided and some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security are discussed.
Abstract: Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.
1,067 citations
01 Mar 2003
TL;DR: In some applications, biometrics can replace or supplement the existing technology and in others, it is the only viable approach.
Abstract: Biometrics offers greater security and convenience than traditional methods of personal recognition. In some applications, biometrics can replace or supplement the existing technology. In others, it is the only viable approach. But how secure is biometrics? And what are the privacy implications?.
974 citations
"Fingerprint feature extraction and ..." refers background in this paper
...In many civilian and forensic applications, person identification (1:Many) is required rather than verification (1:1) [3]....
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25 Aug 1996
TL;DR: A new structural approach to the fingerprint classification problem is presented, where from the segmentation of the directional image a relational graph compactly summarising the macro-structure of the fingerprint is derived.
Abstract: A new structural approach to the fingerprint classification problem is presented. The fingerprint directional image is segmented in regions by minimizing the variance of the element directions within the regions. From the segmentation of the directional image a relational graph compactly summarising the macro-structure of the fingerprint is derived. Inexact graph matching techniques can be adopted to compare the obtained graph with the model graphs in order to classify the fingerprint, according to a given classification scheme.
136 citations
"Fingerprint feature extraction and ..." refers background or methods in this paper
...Maio and Maltoni [15] presented an idea of structural approach for classification....
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...Senior used hidden Markov models for fingerprint classification [15]....
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