论文标题
M^2 Deep-ID：使用卷积深神经网络的多视图识别的新型模型
M^2 Deep-ID: A Novel Model for Multi-View Face Identification Using Convolutional Deep Neural Networks
论文作者
论文摘要
尽管深度识别系统（DFR）系统取得了重大进展，但在特定限制（例如不同姿势）下引入新的DFR仍然是一个巨大的挑战。尤其是，由于人头的3D性质，同一主题的面部外观将投影到摄像机图像平面时会引入高层内变异性。在本文中，我们提出了一个新的多视图深面识别（MVDFR）系统，以应对上述挑战。在这种情况下，每个主题在不同视图下的多个2D图像都被馈入所提出的深层神经网络，其独特的设计可以重新表达单个和更紧凑的面部描述符中的面部特征，这反过来又为使用卷积神经网络提供了一种更有信息和抽象的面部识别方式。为了将我们提出的系统的功能扩展到多视图面部图像，在我们提出的模型中修改了黄金标准的深ID模型。实验结果表明，我们提出的方法的精度为99.8％，而最先进的方法的精度为97％。我们还收集了伊朗科学技术大学（IUST）面对数据库，其中有6552张504个受试者的图像来完成我们的实验。
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.
