Face–Iris Multimodal Biometric Identification System
Open accessAbstract
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.
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A biometric system was introduced that fuses face and iris traits to establish a person's identity.
“The authors of [93] introduced a biometric system that combines biometric traits from different modalities (face and iris) to establish an individual’s identity.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [93]
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Sorted by citations
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Ammour, B., Boubchir, L., Bouden, T., & Ramdani, M.
- Affiliations
- University of Jijel (DZ)
- Université Paris 8 (FR)
- Badji Mokhtar-Annaba University (DZ)
- Corresponding author
- Boubchir, Larbi
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 85
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W2997649833
- Cited by
- 111
- References
- 45
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 21 · 2023: 21 · 2022: 19 · 2021: 22 · 2020: 5
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- multimodal biometrics, face recognition, iris recognition, feature extraction, log-Gabor filters, singular spectrum analysis, wavelet transform, hybrid feature fusion, ORL face database, FERET face database
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