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Despite significant advances in Deep Face Recognition (DFR) systems,introducing new DFRs under specific constraints such as varying pose stillremains a big challenge. Most particularly, due to the 3D nature of a humanhead, facial appearance of the same subject introduces a high intra-classvariability when projected to the camera image plane. In this paper, we proposea new multi-view Deep Face Recognition (MVDFR) system to address the mentionedchallenge. In this context, multiple 2D images of each subject under differentviews are fed into the proposed deep neural network with a unique design tore-express the facial features in a single and more compact face descriptor,which in turn, produces a more informative and abstract way for faceidentification using convolutional neural networks. To extend the functionalityof our proposed system to multi-view facial images, the golden standard Deep-IDmodel is modified in our proposed model. The experimental results indicate thatour proposed method yields a 99.8% accuracy, while the state-of-the-art methodachieves a 97% accuracy. We also gathered the Iran University of Science andTechnology (IUST) face database with 6552 images of 504 subjects to accomplishour experiments.

M^2 Deep-ID: A Novel Model for Multi-View Face Identification Using Convolutional Deep Neural Networks