A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion

Fingerprint classification represents an important preprocessing step in fingerprint identification, which can be very helpful in reducing the cost of searching large fingerprint databases. Over the past years, several different approaches have been proposed for extracting distinguishable features and improving classification performance. In this paper, we present a comparative study involving four different feature extraction methods for fingerprint classification and propose a rank-based fusion scheme for improving classification performance. Specifically, we have compared two well-known feature extraction methods based on orientation maps (OMs) and Gabor filters with two new methods based on "minutiae maps" and "orientation collinearity". Each feature extraction method was compared with each other using the NIST-4 database in terms of accuracy and time. Moreover, we have investigated the issue of improving classification performance using rank-level fusion. When evaluating each feature extraction method individually, OMs performed the best. Gabor features fell behind OMs mainly because their computation is sensitive to errors in localizing the registration point. When fusing the rankings of different classifiers, we found that combinations involving OMs improve performance, demonstrating the importance of orientation information for classification purposes. Overall, the best classification results were obtained by fusing orientation map with orientation collinearity classifiers.