  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.
