Quality Assessment of Fingerprint Images Using Local Texture Descriptors

Abstract

Analyzing the fingerprint quality is of paramount importance as it affects recognition performance. The low-quality fingerprint images degrade the recognition performance as they produce spurious minutiae points. Therefore, estimation of fingerprint quality is essential to avoid performance degradation. Local texture descriptors utilizing micro-textural features for analyzing texture patterns are attaining popularity due to their flexibility and excellent performance. The proposed work aims at evaluating the competency of two well known texture descriptors, namely, Weber Local Descriptor (WLD) and Binarized Statistical Image Features (BSIFs) for fingerprint quality assessment. Computation of WLD features is inspired from the Weber’s law which considers human visual perception of texture patterns while BSIFs are computed by automatically learning a predefined set of filters from a set of natural images instead of using manual filters. The features extracted using WLD and BSIFs are utilized individually to assess dry, wet, and good texture quality of fingerprint blocks. The fingerprint blocks of different qualities are classified into suitable quality classes using Support Vector Machine (SVM) classifier. Thereafter, block texture quality assessment method is used iteratively for fingerprint texture quality assessment. The experimental evaluations performed on publicly available low-quality FVC 2004 fingerprint data-sets show that proposed method outperforms other state-of-the-art methods of fingerprint quality assessment. © 2020, Springer Nature Switzerland AG.