NYONGESA, H. O., AL-KHAYATT, S., MOHAMED, S. M. and MAHMOUD, M. (2004). Fast robust fingerprint feature extraction and classification. Journal of intelligent and robotic systems, 40 (1), 103-112.Full text not available from this repository.
Automatic identification of humans based on their fingers is still one of the most reliable identification methods in criminal and forensic applications. Identification by fingerprint involves two processes: fingerprint feature extraction and feature classification. The basic idea of fingerprint feature extraction algorithms proposed is to locate the coarse features of fingerprints called singular-points using directional fields of the fingerprint image. The features are then classified by different types of neural networks. The "five-class" classification problem is addressed on the NIST-4 database of fingerprints. A maximum classification accuracy of 93.75% was achieved and the result shows a performance comparable to previous studies using either coarse features or the finer features called minutiae.
|Research Institute, Centre or Group:||Cultural Communication and Computing Research Institute > Communication and Computing Research Centre|
|Depositing User:||Ann Betterton|
|Date Deposited:||17 Nov 2010 14:55|
|Last Modified:||17 Nov 2010 14:55|
Actions (login required)
Downloads per month over past year