M^2 Deep-ID: A Novel Model for Multi-View Face Identification Using Convolutional Deep Neural Networks
This addresses the problem of high intra-class variability in face identification for security or biometric applications, but it is incremental as it modifies an existing Deep-ID model.
The paper tackles the challenge of face recognition under varying poses by proposing a multi-view deep face recognition system that achieves 99.8% accuracy, outperforming the state-of-the-art at 97%.
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.