A Study on the Fingerprint Recognition Method Directional Feature Detection using Neural Networks

Fingerprint-based identification is known to be used for a very long time. Owing to their uniqueness and immutability, fingerprints are today the most widely used biometric features. Therefore, recognition using fingerprints is one of the safest methods as a way of personal identification. In this paper, a fingerprint identification method using neural networks and the direction feature vectors based on the directional image extracted from gray-scale fingerprint image without binarization and thinning is proposed. The basic idea of the above mentioned method is to track the ridge lines on the gray-scale image, by ？ailing according to the local orientation of the ridge pattern. A set of starting points are determined by superimposing a grid on the gray-scale image. A labeling strategy is adopted to examine each ridge line only once and locate the intersections between ridge lines. After the direction feature vectors are consisted of vectors by four direction labeling. Matching method used in this paper is four direction feature vectors based matching. The experiment are used total 124 feature patterns of four fingerprints, and One fingerprint image is consisted of 31 feature patterns. The results is presented excellent recognition capability of learned fingerprint images.Abstract(Korean) = 2 Abstract(English) = 3 Chapter 1 Introduction = 4 Chapter 2 Neural networks = 6 2.1 Introduction of neural networks = 6 2.2 Investigation between biological and artificial neuron = 7 2.3 Learning and structure of multilayer neural network = 10 2.4 Multilayered neural networks used experimental = 14 Chapter 3 Fingerprint recognition = 15 3.1 Direction feature vector detection = 15 3.2 Tangent direction computation = 18 3.3 Four direction labeling and pattern detection = 20 Chapter 4 Experimental results = 25 4.1 Experimental environment and method = 25 4.2 Experimental results = 29 Chapter 5 Conclusion = 40 References = 4