Perfect fingerprint orientation fields by locally adaptive global models. Issue 3 (14th December 2016)

• Record Type: Journal Article
• Authors: Gottschlich, Carsten; Tams, Benjamin; Huckemann, Stephan
• Is Part Of: IET biometrics. Volume 6:Issue 3(2017)
• Page Start: 183
• Page End: 190
• Publication Date: 2016-12-14
• Subjects: fingerprint identification -- image enhancement -- image matching -- image coding -- data compression; fingerprint recognition -- perfect fingerprint orientation fields -- adaptive global models -- image enhancement -- fingerprint alignment -- fingerprint liveness detection -- fingerprint alteration detection -- fingerprint matching -- ground truth OF -- geometric interpretation; B6135C -- Image and video coding -- B6135E -- Image recognition -- C5260B -- Computer vision and image processing techniques -- C6130 -- Data handling techniques; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2016.0087
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the 'true OF' in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high‐fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi‐automatically, depending on image quality, and they establish the main underlying claim of high‐fidelity low parameter OF compression.