Combining singular points and orientation image information for fingerprint classification

Fingerprint classification is crucial to reduce the processing time in a large-scale database. Two popular features used are the singularities and orientation information and they are complementary. Therefore, an algorithm based on the interactive validation of singular points and the constrained nonlinear orientation model is proposed. The final features used for classification comprises the coefficients of the orientation model and the singularity information. This resulted in very compact feature vector which is used as input to an SVM classifier to perform the classification. The experiments conducted on the NIST database 4 show the effectiveness of the proposed method in producing good classification result.