  Despite significant advances in Deep Face Recognition (DFR) systems,
introducing new DFRs under specific constraints such as varying pose still
remains a big challenge. Most particularly, due to the 3D nature of a human
head, facial appearance of the same subject introduces a high intra-class
variability when projected to the camera image plane. In this paper, we propose
a new multi-view Deep Face Recognition (MVDFR) system to address the mentioned
challenge. In this context, multiple 2D images of each subject under different
views are fed into the proposed deep neural network with a unique design to
re-express the facial features in a single and more compact face descriptor,
which in turn, produces a more informative and abstract way for face
identification using convolutional neural networks. To extend the functionality
of our proposed system to multi-view facial images, the golden standard Deep-ID
model is modified in our proposed model. The experimental results indicate that
our proposed method yields a 99.8% accuracy, while the state-of-the-art method
achieves a 97% accuracy. We also gathered the Iran University of Science and
Technology (IUST) face database with 6552 images of 504 subjects to accomplish
our experiments.
