Next Article in Journal
Design of an Intrinsically Safe Series-Series Compensation WPT System for Automotive LiDAR
Previous Article in Journal
Development of a High-Performance, FPGA-Based Virtual Anemometer for Model-Based MPPT of Wind Generators
Previous Article in Special Issue
Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features
Open AccessFeature PaperArticle

Face–Iris Multimodal Biometric Identification System

1
NDT Laboratory, Electronics Department, Jijel University, Jijel 18000, Algeria
2
LIASD Laboratory, Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France
3
NDT Laboratory, Automatics Department, Jijel University, Jijel 18000, Algeria
4
LASA Laboratory, Badji Mokhtar-Annaba University, Annaba 23000, Algeria
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 85; https://doi.org/10.3390/electronics9010085
Received: 28 October 2019 / Revised: 15 December 2019 / Accepted: 17 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness. View Full-Text
Keywords: multimodal biometrics; biometric identification; iris; face; feature extraction; fusion; singular spectrum analysis; normal inverse Gaussian; wavelet multimodal biometrics; biometric identification; iris; face; feature extraction; fusion; singular spectrum analysis; normal inverse Gaussian; wavelet
Show Figures

Figure 1

MDPI and ACS Style

Ammour, B.; Boubchir, L.; Bouden, T.; Ramdani, M. Face–Iris Multimodal Biometric Identification System. Electronics 2020, 9, 85.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop