Title: A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA
Authors: K. Sasirekha; K. Thangavel
Addresses: Department of Computer Science, Periyar University, Salem, Tamilnadu, India ' Department of Computer Science, Periyar University, Salem, Tamilnadu, India
Abstract: Fingerprint classification is an important indexing scheme to reduce fingerprint matching time. In this paper, a novel approach to classify fingerprint images is proposed. It involves four main parts: denoising, feature extraction, dimensionality reduction and classification. Initially, the fingerprint is denoised using undecimated wavelet transform. Then short time Fourier transform (STFT) is used to enhance the denoised fingerprints. A set of local binary pattern (LBP) features are extracted to overcome the difficulty associated with singular point detection. To reduce the dimensionality of the feature space, quick reduct (QR), principal component analysis (PCA) and weighted PCA have been investigated. Finally, the fingerprint images are classified using back propagation neural network (BPNN). In this research, experiments have been conducted on real-time fingerprint images collected from 150 subjects and also on the NIST-4 dataset. The proposed method has been compared with support vector machine (SVM), K-nearest neighbor (K-NN), and multi-layer perceptron (MLP).
Keywords: fingerprint; classification; local binary pattern; LBP; back propagation neural network; BPNN; quick reduct; weighted PCA.
International Journal of Biometrics, 2018 Vol.10 No.1, pp.77 - 104
Available online: 20 Feb 2018 *Full-text access for editors Access for subscribers Free access Comment on this article