Study On Some Key Problems In Fingerprint Recognition

While a significant progress has been made in the research and development of automatic fingerprint recognition, the application of the technology does not prevail at present. The reason is that the accuracy and speed of recognition is far from satisfactory to many practical circumstances. To improve the performance of automatic fingerprint recognition is meaningful both in theory aspect and promoting its application. For this reason, this thesis has discussed the problems of fingerprint image enhancement, fingerprint minutiae extraction and verification, fingerprint minutiae matching and fingerprint classification, using the knowledge of digital image processing, pattern recognition and computational intelligence. The major contributions of this thesis are listed in the following:(1) A minutiae extraction method based on binary image is proposed for the first time. Firstly the image segments are extracted from binary image based on run-length code matching where each segment represents a section of fingerprint ridge without bifurcation. Then the minutiae are determined by defined rules based on the structure of these segments and their link relations. Because no need to perform image thinning, the method is more faster and can avoid many spurious minutiae caused by thinning aberrance. Experimental results show its efficiency. The method provided a new way to fingerprint minutiae extraction.(2) A new minutiae verification method based on the fuzzy geometry features and texture features is proposed. Firstly take the local neighbor area of a minutia in the gray image. Then analyze the fuzzy geometry features and texture features in the local area and use these features as the input of a MLP neural network to realize the classification of true and spurious minutiae. Experimental results show that the proposed method is better than a method which using pixels in the local area directly.(3) A minutiae matching method combining genetic algorithm and fuzzy logic is proposed. Firstly using a modified points matching method based on genetic algorithm to find the best minutiae corresponding relations which having the most matching pairs and minimum matching error. Then reason the matching score based on defined fuzzylogic relations between the number of matching pairs, the matching error and the matching score. This method simulate the uncertainty of human decision-making when compare two fingerprint, so it's more reasonable. Experimental results show that the method is accurate.(4) A fingerprint image enhancement method based on Gabor filter is presented and realized. This method estimates the orientation and orientation certainty at each pixel, computes the average ridge frequency. Then adjust the parameters of Gabor filter using the orientation and average ridge frequency at each pixel to realize adaptive filtering. The unrecoverable region is segmented based on orientation certainty. Experimental results show a very good visual enhancement effect.(5) A fingerprint classification method based on singular points and central symmetrical axis is presented and realized. If sufficient singular points can be extracted from fingerprint image, then classify fingerprint mainly based on singular points. Otherwise classify fingerprint based on central symmetrical axis. Experimental results show that the method is more efficiency.Finally, summarize the work of this thesis, analyze the improvements need to be done and give the directions of future work.