Title :
Fingerprint identification: classification vs. indexing
Author :
Tan, Xuejun ; Bhanu, Bir ; Lin, Yingqiang
Author_Institution :
Center for Res. in Intelligence Syst., California Univ., Riverside, CA, USA
Abstract :
We present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprints into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm, based on least square minimization over each of the possible minutiae triplet pairs, is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better, based on the size of the search space and identification results.
Keywords :
feature extraction; fingerprint identification; least squares approximations; minimisation; pattern classification; composite feature detection; composite operators; feature-learning algorithm; fingerprint classification; fingerprint identification; fingerprint indexing; fingerprint verification; image processing operations; least square minimization; minutiae triplets; Fingerprint recognition; Genetic programming; Image processing; Indexing; Intelligent systems; Least squares methods; Minimization methods; Spatial databases; Uncertainty; Videoconference;
Conference_Titel :
Advanced Video and Signal Based Surveillance, 2003. Proceedings. IEEE Conference on
Print_ISBN :
0-7695-1971-7
DOI :
10.1109/AVSS.2003.1217915