Fingerprint classification based on multiple discriminant analysis
01 Jan 2002-Vol. 5, pp 2469-2473
TL;DR: An effective fingerprint classification method based on Multiple Discriminant Analysis (MDA) is presented and the typology information is used to classify fingerprints and a feature extraction method is described based on Gabor filters.
Abstract: In this paper an effective fingerprint classification method based on Multiple Discriminant Analysis (MDA) is presented. The typology information is used to classify fingerprints. We also describe a feature extraction method based on Gabor filters. The effectiveness of our method is based on matters, that a reference point is searched from the region of interest and after finding the reference point, we crop the smaller image below the reference point. Then every operation, like feature calculation, is done only to this smaller image. The method was tested using artificially generated fingerprint database. With a uniform fingerprint distribution our classifier works an accuracy of 95.8% for the five-class problem.
Citations
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17 Sep 2007
TL;DR: This paper proposed a lexicon-guided two-level LDA retrieval framework, which uses the HowNet to guide the first level LDA model's parameter estimation, and further constructs the second layer LDA models based on the first-level's inference results.
Abstract: Topic-based language model has attracted much attention as the propounding of semantic retrieval in recent years. Especially for the ASR text with errors, the topic representation is more reasonable than the exact term representation. Among these models, Latent Dirichlet Allocation(LDA) has been noted for its ability to discover the latent topic structure, and is broadly applied in many text-related tasks. But up to now its application in information retrieval(IR) is still limited to be a supplement to the standard document models, and furthermore, it has been pointed out that directly employing the basic LDA model will hurt retrieval performance. In this paper, we propose a lexicon-guided two-level LDA retrieval framework. It uses the HowNet to guide the first-level LDA model's parameter estimation, and further construct the second-level LDA models based on the first-level's inference results. We use TRECID 2005 ASR collection to evaluate it, and compare it with the vector space model(VSM) and latent semantic Indexing(LSI). Our experiments show the proposed method is very competitive.
18Â citations
15 Jul 2011
TL;DR: A sequence flow diagram is presented which will help in developing the clarity on designing algorithm for classification based on various parameters extracted from the fingerprint image.
Abstract: Classification refers to associating a given fingerprint to one of the existing classes already recognized in the literature. A search over all the records in the database takes a long time, so the aim is to reduce the size of the search space by choosing an appropriate subset of database for search. Classifying a fingerprint images is a very difficult pattern recognition problem, due to the small interclass variability, the large intraclass variability. This paper presents a sequence flow diagram which will help in developing the clarity on designing algorithm for classification based on various parameters extracted from the fingerprint image. It discusses in brief the ways in which the parameters are extracted from the image. Existing fingerprint classification approaches are based on these parameters as input for classifying the image. Parameters like orientation map, singular points, spurious singular points, ridge flow and hybrid feature are discussed in the paper.
10Â citations
Cites methods from "Fingerprint classification based on..."
...Feature Vector is obtained by finding the region of interest [ 24 ] using core point....
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17 Sep 2007
TL;DR: Experimental results have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
Abstract: In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce further computational load requirements, and (iii) conduct supervised classification with the C-RSPM (Collateral Representative Subspace Projection Modeling) approach. Experimental results on a variety of data sets have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
8Â citations
Cites methods from "Fingerprint classification based on..."
...In [13], an effective fingerprint classification method was proposed based on MDA, where features were calculated from Gabor filtered images and the derived feature vectors were classified into one of five classes....
[...]
TL;DR: A sequence flow diagram is presented which will help in developing the clarity on designing algorithm for classification based on various parameters extracted from the fingerprint image.
Abstract: Classification refers to assigning a given fingerprint to one of the existing classes already recognized in the literature. A search over all the records in the database takes a long time, so the goal is to reduce the size of the search space by choosing an appropriate subset of database for search. Classifying a fingerprint images is a very difficult pattern recognition problem, due to the minimal interclass variability and maximal intraclass variability. This paper presents a sequence flow diagram which will help in developing the clarity on designing algorithm for classification based on various parameters extracted from the fingerprint image. It discusses in brief the ways in which the parameters are extracted from the image. Existing fingerprint classification approaches are based on these parameters as input for classifying the image. Parameters like orientation map, singular points, spurious singular points, ridge flow, transforms and hybrid feature are discussed in the paper.
6Â citations
27 Mar 2010
TL;DR: An effective algorithm based on block level for fingerprint reference point detection is proposed to simplify detection process and improve accuracy of the position of the point.
Abstract: It is very important to detect the reference point effectively and accurately, especially in nonminutiae based fingerprint matching and fingerprint classification. In this paper, an effective algorithm based on block level for fingerprint reference point detection is proposed to simplify detection process and improve accuracy of the position of the point. The method is simple in the preprocessing and easy to implement. We used the Poincare Index on the block level, which is combined with the adaptive smoothing for getting a better orientation map and the directional consistency factor with the purpose of choosing the correct block. The proposed algorithm has been tested on fvc2002 and fvc2004 database. Experimental results show it is effective and practical.
5Â citations
References
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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.
2,212Â 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.
1,207Â citations
TL;DR: A Gabor filter-based method for directly extracting fingerprint features from grey-level images without pre-processing is introduced and shows that the recognition rate of the k-nearest neighbour classifier using the proposed Gabor Filter-based features is 97.2%.
Abstract: A Gabor filter-based method for directly extracting fingerprint features from grey-level images without pre-processing is introduced. The proposed method is more efficient and suitable than conventional methods for a small-scale fingerprint recognition system. Experimental results show that the recognition rate of the k-nearest neighbour classifier using the proposed Gabor filter-based features is 97.2%.
179Â citations
13 May 2002
TL;DR: The algorithm is based on the fact that the fingerprint ridges are regions where the second directional derivative of the image is positive, and it was noticed that the size of this neighborhood affects critically to the results.
Abstract: An efficient method for binarization of the fingerprint images is presented in this paper. The algorithm is based on the fact that the fingerprint ridges are regions where the second directional derivative of the image is positive. The derivatives at each pixel are approximated using a facet model based on the intensity values of pixels in a certain neighborhood. It was noticed that the size of this neighborhood affects critically to the results. The size of the neighborhood is depended on the inter-ridge distance. The method based on the averaging in the direction of the ridges was used to determine inter-ridge distances. Using these inter-ridge distances, size of the neighborhood in the binarization algorithm was calculated for each pixel. Ridge directions were calculated using the gradient information of the image. The algorithm was tested using digitally acquired fingerprints. The results show that the algorithm is capable to produce very accurate binarization results.
6Â citations