Applying learning vector quantization neural network for fingerprint matching

Authors:
Ju Cheng Yang;Sook Yoon;Dong Sun Park
Affiliations:
Dept. of Infor.& Comm.Eng., Chonbuk National University, Jeonju, Jeonbuk, Korea;Major in Multimedia Eng., Dept. of Infor. Eng., Mokpo National University, Jeonnam, Korea;Dept. of Infor.& Comm.Eng., Chonbuk National University, Jeonju, Jeonbuk, Korea
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 4
Cited 1

Digital Image Processing

Digital Image Processing
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Localization of corresponding points in fingerprints by complex filtering

Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Filterbank-based fingerprint matching

IEEE Transactions on Image Processing

A fingerprint verification algorithm using tessellated invariant moment features

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel method for fingerprint matching using Learning Vector Quantization (LVQ) Neural Network (NN) is proposed. A fingerprint image is preprocessed to remove the background and to enhance the image by eliminating the LL4 sub-band component of a hierarchical Discrete Wavelet Transform (DWT). Seven invariant moment features, called as a fingerCode, are extracted from only a certain region of interest (ROI) of the enhanced fingerprint. Then an LVQ NN is trained with the feature vectors for matching. Experimental results show the proposed method has better performance with faster speed and higher accuracy comparing to the Gabor feature-based fingerCode method.