Deep representations for cross‐spectral ocular biometrics. Issue 2 (7th February 2020)

• Record Type: Journal Article
• Authors: Zanlorensi, Luiz A.; Lucio, Diego Rafael; Britto Junior, Alceu de Souza; Proença, Hugo; Menotti, David
• Is Part Of: IET biometrics. Volume 9:Issue 2(2020)
• Page Start: 68
• Page End: 77
• Publication Date: 2020-02-07
• Subjects: image representation -- learning (artificial intelligence) -- image matching -- biometrics (access control) -- feature extraction -- iris recognition -- eye -- neural nets -- face recognition; cross‐spectral ocular biometrics -- cross‐spectral ocular verification methods -- known deep learning representations -- nonnormalised iris–periocular regions -- publicly available cross‐spectral -- ResNet‐50 deep representations -- PolyU bi‐spectral -- cross‐eye–cross‐spectral datasets; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2019.0116
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : One of the major challenges in ocular biometrics is the cross‐spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross‐spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non‐normalised iris–periocular regions, the authors fine‐tune convolutional neural network models, originally trained for face recognition. On the basis of the experiments carried out in two publicly available cross‐spectral ocular databases, they report results for intra‐spectral and cross‐spectral scenarios, with the best performance being observed when fusing ResNet‐50 deep representations from both the periocular and iris regions. When compared to the state of the art, they observed that the proposed solution consistently reduces the equal error rate values by 90%/93%/96% and 61%/77%/83% on the cross‐spectral scenario and in the PolyU bi‐spectral and cross‐eye–cross‐spectral datasets. Finally, they evaluate the effect that the 'deepness' factor of feature representations has in recognition effectiveness, and based on a subjective analysis of the most problematic pairwise comparisons – they point out further directions for this field of research.