Fingerprint classification based on statistical features and singular point information

Automatic fingerprint classification is an effective means to increase the matching speed of an Automatic Fingerprint Identification System with a large-scale fingerprint database. In this paper, an automatic fingerprint classification method is proposed to classify the fingerprint image into one of five classes: Arch, Left loop, Right Loop, Whorl and Tented Arch. First, the information of core points, which is detected with a two-stage method, is applied to determine the reference point in fingerprint image. Then three different features based on statistical properties of small image blocks, which are likely to degrade with image quality deterioration, are calculated from the region of interest and form a 300-dimension feature vector. The feature vector is inputted into a three-layer Back Propagation Network (BPN) classifier and a 5-dimension vector is outputted, each dimension of which corresponds to one of 5 fingerprint classes. Finally, the fingerprints are classified with integrate analysis of the BPN classifier output and singular point information. The accuracy of 93.23% with no rejection is achieved on NIST-4 database and experimental results show that the proposed method is feasible and reliable for fingerprint classification.