Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis

TL;DR

This paper proposes a two-stage, quality-adaptive fingerprint enhancement method that uses fuzzy c-means clustering to classify images by quality and applies tailored enhancement techniques, significantly improving verification accuracy on FVC2004 datasets.

Contribution

It introduces a novel fuzzy c-means based clustering for fingerprint quality assessment and a two-stage adaptive enhancement process tailored to each quality class.

Findings

Improved verification accuracy on FVC2004 datasets.

Significant reduction in equal error rate with adaptive preprocessing.

Enhanced fingerprint recognition performance for poor quality images.

Abstract

Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, normal wet and good quality using fuzzy c-means technique. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity and ridge valley uniformity into account for clustering the fingerprint images in appropriate quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement process. A quality adaptive preprocessing method is used as front-end before enhancing the fingerprint images with Gabor, short term Fourier transform and oriented diffusion filtering based enhancement techniques. Experimental results show improvement in the verification results for FVC2004 datasets. Significant improvement in equal error rate is observed while using quality adaptive preprocessing based approaches in comparison to the current state-of-the-art enhancement techniques.