Skip to main content

Iris liveness detection using regional features

Hu, Yang, Sirlantzis, Konstantinos, Howells, Gareth (2015) Iris liveness detection using regional features. Pattern Recognition Letters, . ISSN 0167-8655. (doi:10.1016/j.patrec.2015.10.010) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.patrec.2015.10.010

Abstract

In this paper, we exploit regional features for iris liveness detection. Regional features are designed based on the relationship of the features in neighbouring regions. They essentially capture the feature distribution among neighbouring regions. We construct the regional features via two models: spatial pyramid and relational measure which seek the feature distributions in regions with varying size and shape respectively. The spatial pyramid model extracts features from coarse to fine grid regions, and, it models a local to global feature distribution. The local distribution captures the local feature variations while the global distribution includes the information that is more robust to translational transform. The relational measure is based on a feature-level convolution operation defined in this paper. By varying the shape of the convolution kernel, we are able to obtain the feature distribution in regions with different shapes. To combine the feature distribution information in regions with varying size and shape, we fuse the results based on the two models at the score level. Experimental results on benchmark datasets demonstrate that the proposed method achieves an improved performance compared to state-of-the-art features.

Item Type: Article
DOI/Identification number: 10.1016/j.patrec.2015.10.010
Uncontrolled keywords: Iris liveness detection; Regional feature; Local descriptors
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.B56 Biometrics
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.P3 Pattern Recognition
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Konstantinos Sirlantzis
Date Deposited: 13 Dec 2015 13:23 UTC
Last Modified: 29 May 2019 16:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53246 (The current URI for this page, for reference purposes)
  • Depositors only (login required):