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

A Vision System for Multi-View Face Recognition