Bag-of-Visual-Words Model for Fingerprint Classification

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.