Bag-of-Visual-Words Model for Fingerprint
Classification
Pulung Andono and Catur
Supriyanto
Department of Computer Science, University of Dian
Nuswantoro, Indonesia
Abstract: In this paper,
fingerprint classification based on Bag-of-Visual-Word (BoVW) model is
proposed. In BoVW, an image is represented as a vector of occurrence count of
features or words. In order to extract the features, we use Speeded-Up Robust
Feature (SURF) as the features descriptor, and Contrast Limited Adaptive
Histogram Equalization (CLAHE) to enhance the quality of fingerprint images. Most
of the fingerprint research areas focus on Henry’s classification instead of
individual person as the target of classification. We present the evaluation of
clustering algorithms such as k-means, fuzzy c-means, k-medoid and hierarchical
agglomerative clustering in BoVW model for FVC2004 fingerprint dataset. Our
experiment shows that k-means outperforms than other clustering algorithms. The
experimental result on fingerprint classification obtains the performance of
90% by applying k-means as features descriptor clustering. The results show
that CLAHE improves the performance of fingerprint classification. The using of
public dataset in this paper makes opportunities to conduct the future
research.
Keywords: Fingerprint classification; bag of visual
word model; clustering algorithm; speeded-up
robust feature; contrast limited adaptive histogram equalization.