A Study of Unsupervised Fingerprint Image Clustering

• Main Authors: Eng-Liang Chuang, 莊英良
• Other Authors: 黃文增
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/b2ge53

碩士 === 國立臺北科技大學 === 通訊與資訊產業研發碩士專班 === 96 === The applications of fingerprint identification become more and more important today. Fingerprint identification not only applies to information security but also contributes to criminal investigation, such as suspect matching . However the fingerprint database is getting larger, it is necessary to manage and increase the speed of matching. In this dissertation, we discuss the method of automatic clustering fingerprints without clustering by person. We cluster the fingerprints which is unknown and make all fingerprints in a cluster that belongs to the same person. If we want to check or mark a fingerprint belonged to whom, we only need to check it’s group by group instead of checking it one by one. So it can save lots of manpower and time cost. In the content, it will present to utilize structural matching and hierarchical agglomerative clustering algorithms to build clusters of fingerprints which base on similarity so as to support a great quantity of fingerprint and the method would be beneficial to search high similarity fingerprint quickly, and furthermore, the cluster of combination can improve the speed of fingerprint matching. In the dissertation , image processing will be applied by several methods, such as : histogram equalization, binarization, normalization, thinning ,etc. Input the fingerprint image and proceed with a large quantity of image enhancement so that obtain the related features. Then use these related features, for example: termination, bifurcation, to compose a feature structure separately. Finally, put the feature structure of two fingerprints into a minutia matching to get the degree of similarity. Base on hierarchical agglomerative clustering algorithm, the fingerprint can be clustered according to the similarity. Moreover, cluster purity and rand index can be provided to be a norm of the quality of cluster.