Combining Gabor filtering and classification dictionaries learning for fingerprint enhancement. Issue 6 (25th May 2017)

• Record Type: Journal Article
• Authors: Ding, Shifei; Bian, Weixin; Liao, Hongmei; Sun, Tongfeng; Xue, Yu
• Is Part Of: IET biometrics. Volume 6:Issue 6(2017)
• Page Start: 438
• Page End: 447
• Publication Date: 2017-05-25
• Subjects: fingerprint identification -- image classification -- frequency‐domain analysis -- image representation -- image enhancement; Gabor filtering -- classification dictionaries learning -- fingerprint image enhancement -- sparse representation -- ridge pattern -- fingerprint patch orientations -- weighted linear projection analysis -- training patch classification -- frequency domain -- spectra diffusion -- automatic fingerprint identification system -- performance improvement; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2016.0161
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : This study presents a new method for enhancing fingerprint image. The process of the enhancement is divided into two phases: fingerprint is first enhanced using Gabor filtering and then the enhanced fingerprint can be further enhanced by using sparse representation with the priori information of ridge pattern based on classification dictionaries learning. In the second stage, first, the orientations of fingerprint patches are estimated by the weighted linear projection analysis and the quality of patches are evaluated by the coherence of point orientations. Second, the training patches are classified into eight groups based on their own orientations, and the training samples of each class are selected from candidate patches by their own quality. The corresponding classification dictionaries are learned in frequency domain. Finally, the fingerprint image is enhanced based on spectra diffusion by using classification dictionaries learning. The experiments are carried out using various fingerprint enhancement methods. The experiments show that the proposed method achieves better results in comparison with other methods, and can significantly improve the performance of automatic fingerprint identification system.