期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：11
出版社：S.S. Mishra
摘要：Fingerprint matching is an important and challenging problem in fingerprint recognition. Even though so many different methods are there, it has been learned from studies that a better feature extraction technique may leads to very good result. A biometric system is essentially a pattern recognition system that operates by acquiring biometric data from an individual, extracting a feature set from the acquired data, and comparing this feature set against the template set in the database. Depending on the application context, a biometric system may op erate in identification mode. An automatic fingerprint identification system is widely adopted in many applications such as building or area security and ATM machines. Fingerprint classification is an important step in any fingerprint identification system because it extensively reduces the time taken in identification of fingerprints mainly where the precision and speed are important. Classification allows an input fingerprint to be matched against only by a subset of a database and is important in speeding-up fingerprint identification. Conversely, classification is not enough to identify a fingerprint; it is useful in deciding when two fingerprints do not match. To reduce the search and space complexity, a efficient partitioning of the database into different classes is highly essential. Key to the task of classification is the feature extraction. The effectiveness of feature extraction depends on the quality of the images, representation of the image data, the image processing models, and the evaluation o f the extracted features. At the first stage of the fingerprint classification process, the image is only represented as a matrix of grey scale intensity values. Feature extraction is a process through which geometric primitives within images are isolated in order to describe the image structure, i.e. to extract important image information and to suppress redundant information that are not useful for classification and identification processes. Thus fingerprint features and their relationships provide a rep resentative explanation of a fingerprint image. In this paper, finger print recognition and matching algorithm is explained and results give remarkable performance. Images are cropped and features are extracted, then matching is done using Euclidean distance.
