Fingerprints Identification Using Contourlet Transform | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 35, 3A, March 2017, Pages 282-288 PDF (1.05 M) | ||
| DOI: 10.30684/etj.35.3A.14 | ||
| Authors | ||
| T.M. Salman; M.K.M. Al-Azawi | ||
| Abstract | ||
| This paper suggests the use of contourlet transform for efficient feature extraction of fingerprints for identification purposes. Back propagated neural network is then used as a classifier. Two fingerprints databases are used to test the system. These include fingerprints images with different positions, rotations and scales to test the robustness of the system. Computer simulation results show that the proposed contourlet transform outperforms the classical wavelet method. Where an identification rate of 94.4% was obtained using contourlet transform compare with 87% using wavelet transform for standard FVC2002 database. | ||
| Keywords | ||
| Back Prorogation Neural Network Classifier; Contourlet Fingerprint Identification; Contourlet Transform; Discrete Wavelet Transform | ||
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