We introduce our method and system for face recognition using multiple
pose-aware deep learning models. In our representation, a face image is
processed by several pose-specific deep convolutional neural network (CNN)
models to generate multiple pose-specific features. 3D rendering is used to
generate multiple face poses from the input image. Sensitivity of the
recognition system to pose variations is reduced since we use an ensemble of
pose-specific CNN features. The paper presents extensive experimental results
on the effect of landmark detection, CNN layer selection and pose model
selection on the performance of the recognition pipeline. Our novel
representation achieves better results than the state-of-the-art on IARPA's CS2
and NIST's IJB-A in both verification and identification (i.e. search) tasks.

使用多个面向姿态的深度学习模型进行人脸识别，其中处理面部图像生成多个具有不同姿态特点的卷积神经网络特征，并使用这些特征的集合来减少对姿势变化的敏感性。在多个实验中，我们展示了该方法在人脸识别任务的表现优于现有技术。