  Multimodal biometric identification has been grown a great attention in the
most interests in the security fields. In the real world there exist modern
system devices that are able to detect, recognize, and classify the human
identities with reliable and fast recognition rates. Unfortunately most of
these systems rely on one modality, and the reliability for two or more
modalities are further decreased. The variations of face images with respect to
different poses are considered as one of the important challenges in face
recognition systems. In this paper, we propose a multimodal biometric system
that able to detect the human face images that are not only one view face
image, but also multi-view face images. Each subject entered to the system
adjusted their face at front of the three cameras, and then the features of the
face images are extracted based on Speeded Up Robust Features (SURF) algorithm.
We utilize Multi-Layer Perceptron (MLP) and combined classifiers based on both
Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) for
classification purposes. The proposed system has been tested using SDUMLA-HMT,
and CASIA datasets. Furthermore, we collected a database of multi-view face
images by which we take the additive white Gaussian noise into considerations.
The results indicated the reliability, robustness of the proposed system with
different poses and variations including noise images.
