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.

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.

Extract facial feature is considered as the most important step in face recognition systems. Several researches have been made to improve facial feature extraction techniques. This paper proposes hybrid global and local feature based on Gabor filter bank and double coding local binary pattern (LBP). The proposed features are concatenated and reduced using generalized discriminant analysis (GDA). The matching step is then performed using Euclidian distance. Performances evaluation of face identification system is carried out using ORL and FERET databases, and the experimental results shown the high recognition rate of the proposed system.