Face–iris multi‐modal biometric system using multi‐resolution Log‐Gabor filter with spectral regression kernel discriminant analysis. Issue 5 (19th March 2018)

• Record Type: Journal Article
• Authors: Ammour, Basma; Bouden, Toufik; Boubchir, Larbi
• Is Part Of: IET biometrics. Volume 7:Issue 5(2018)
• Page Start: 482
• Page End: 489
• Publication Date: 2018-03-19
• Subjects: iris recognition -- image texture -- regression analysis -- face recognition -- feature extraction -- Gabor filters -- image fusion -- image classification; image fusion -- image classification -- CASIA iris distance database -- multiresolution two‐dimensional log‐Gabor filter -- robust recognition system -- feature extraction -- hybrid level -- texture information -- face–iris multimodal biometric system -- unimodal biometric system -- spectral regression kernel discriminant analysis; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2017.0251
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : A multi‐modal biometric system is used to verify or identify a person by exploiting information of more than one biometric modality. It combines the strengths of the unimodal biometric system to solve their limitations. This study proposes schemes of multi‐modal biometric system based on texture information extracted from face and two iris (left and right) using hybrid level of fusion. Feature extraction is the key step to get a robust recognition system. Multi‐resolution two‐dimensional Log‐Gabor filter combined with spectral regression kernel discriminant analysis is exploited to extract features from both face and iris modalities. These features are used in the fusion and the classification process. The proposed schemes were tested using CASIA Iris Distance database in the verification mode. The experimental results show that the proposed multi‐modal biometric system yields attractive performances of up to 0.24% in terms of equal error rate and outperforms the recent similar existing state‐of‐the‐art methods.