Feature Extraction in .NET

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14.4 Feature Extraction
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Figure 14.8. Minutiae in a ngerprint portion.
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Several methods exist for minutiae extraction from a gray-level ngerprint image [1] and most of them are based on traditional or adhoc image processing techniques. However some learning based minutiae detection approaches have been proposed where a sliding window (see Figure 14.9) is moved on the nger image and each local window is analyzed by a trained classi er to check whether it corresponds to a minutia or not. A set of minutiae manually extracted by a domain expert is used for the classi er training.
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Figure 14.9. A ngerprint portion with a sliding window over a minutia region.
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The rst approaches based on neural networks date back to the early 1990s when Leung et al. [28] used a multilayer perceptron trained by the output of a bank of Gabor s lters applied to the gray-scale ngerprint and Leung et al. [29] used a multilayer perceptron trained by the minutiae extracted from skeletonized binary images. Other pioneering approaches based on arti cial intelligence methods are: image exploring agents and reinforcement learning [30] and image exploring agents and genetic programming [31]. More recently, new learning approaches for minutiae detection have been proposed: r Yang et al. [32] associate to a minutia point both the information from the point itself and also from its surrounding edges. r Burian et al. [33] use a SVM; unlike most of the previous approaches, this method does not require a ridge thinning processing step. r In the work by Carlson et al. [34], three different classi ers are trained and combined: a minimum distance classi er trained by principal component analysis (PCA) features extracted from gray-level data; a neural network classi er trained by PCA features (hereto extracted by gray-level data); and a neural network classi er directly trained by the gray-level data (i.e., without dimensionality reduction by PCA). The individual best result has been obtained by the second classi er; however, the combination (through clustering) of the three classi ers outperforms single classi ers. r In the Bhanu and Tan [35] approach, a set of templates for minutiae extraction are learned from examples by optimizing a criterion function using Lagrange s method. Then for online minutiae extraction, these learned templates are applied to binarized ngerprint images to detect the presence of minutiae. Minutiae detection through local window classi cation is today still not much used by state-of-the-art ngerprint recognition systems because of two main drawbacks: (i) the large amount of false alarms that they can produce especially on poorquality images, and (ii) the high computation time that makes them unsuitable for fast detection. The same drawbacks were initially encountered also in face detection approaches [36], but nowadays the adoption of boosting techniques based on intensive training of many ef cient weak classi ers (e.g., the Viola and Jones algorithms [3] based on Adaboost [37]) allows us to effectively overcome them. The same could happen in the future for ngerprint minutiae detection where the use of new learning-based technique could lead to better performance than conventional image processing-based extractors.
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Minutiae Filtering
Since a large number of spurious minutiae are often located in noising ngerprint images by automatic extraction algorithms, postprocessing techniques can be useful
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14.4 Feature Extraction
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to lter out most of them. Many minutiae ltering methods have been proposed in the literature (see reference 1 for a survey); they can be roughly classi ed into two groups: structural postprocessing and image-based ltering. The former mainly contains rulebased approaches that do not require any learning phase, with the only exception of reference 34 (see Subsection 14.4.1). The latter includes learning approaches similar to those developed for minutiae detection where a classi er is trained to process (i.e., labeling as true or false) image-based features extracted from a small region around each minutia. It is worth noting that here ef ciency is not a concern (as for minutiae detection) because only a small number of regions have to be checked that is, those corresponding to the minutiae detected in the previous stage. Maio and Maltoni [38] reduce the dimensionality of the gray-scale data in the local region around each minutia through principal component analysis (PCA). Classi cation is then performed by a shared weights neural network that uses both positive and negative images of the minutiae neighborhood to exploit the duality of the ridge and bifurcation. Prabhakar et al. [39, 40] rst enhance the region surrounding the minutia by Gabor lters, and then they use learning vector quantizer to lter the minutiae. Another method based on the PCA subspace is proposed by Chikkerur et al. [41], where the dimensionality of features extracted by steerable wedge lters is rst reduced; the resulting vectors are then used to train a neural network. Santhanam et al. [42] propose using an ARTMAP neural network classi er, whose output indicates whether an input region is a termination, a bifurcation, or a false minutia. In the work by Mansukhani et al. [43] a SVM is used for ltering not directly the minutiae but the pair of mated minutiae after the matching stage. The authors of this chapter have recently studied the performance of ltering approaches based on the fusion of different minutiae representations. The following preliminary results have been obtained on a dataset of minutiae extracted from the FVC2002 DB2 [1] by using the minutiae detection algorithm described in [44]. From the rst 50 individuals of FVC2002 DB2, 1500 false positives and 1500 true positives have been manually labeled. The experiments have been carried out according to a vefold cross-validation testing protocol. Starting from the minutiae regions, which are 33 33 pixel windows centered around each minutia, the following feature extraction methods have been evaluated: r DCT: The rst 100 coef cients of the discrete cosine transform [2] with higher variance are retained. r PCA: Dimensionality reduction is performed through PCA [2] by preserving a variance of 0.95. r LEM: Laplacian eigenmaps [45] have been used to project the image onto a lower 100-dimensional space (after the application of a PCA with preserved variance of 0.98). r ICA: Independent component analysis [2] has been used to project the image onto a lower 100-dimensional space (after the application of a PCA with preserved variance of 0.98).
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