Abstract
The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.
Highlights
Fingerprints can be classified in five categories called whorl, right loop, left loop, arch, and tented arch via Henry system [1]
The training vector is defined: Training and Test vector = [Kernel of cropped block from the point (18 numbers), Horizontal Markov chain coefficients extracted from ridges with acute angles (12 numbers), Horizontal Markov chain coefficients extracted from ridges with obtuse angles (12 numbers), Vertical Markov chain coefficients extracted from ridges with acute angles (30 numbers), Vertical Markov chain coefficients extracted from ridges with obtuse angles (30 numbers)] after finding the vertical and horizontal lines, the intersection of these lines is the coordinates around which an n × m block is cropped
Markov chain is a series of states in which each state depends on previous state so that transition matrix is the changes of states which can be achieved by following method [20]: is modeled as two vertical and horizontal Markov chains, demonstrated in figure 10
Summary
Fingerprints can be classified in five categories called whorl, right loop, left loop, arch, and tented arch via Henry system [1]. Procedures like enhancement, feature extraction, and classifiers are common in both classification and recognition. Ridges Orientation might be considered as an advantageous method with the intention of feature extraction [9]. An approach is proposed to extract two lines around which the behavior of ridges and valleys are obtained as a part of final features. Hemad Heidari Jobaneh: Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized. Regression Neural Network and Adaptive Resonance Theory with Mapping smoothing technique, Markov chain, and angular behavior of Ridges and Valleys are used to extract fingerprints features. Generalized regression neural network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP) are used and compared for the sake of recognition. In order to extract the orientation of ridges, the gradient of enhanced images is calculated, shown in figure 4.
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