  Face recognition performance has improved remarkably in the last decade. Much
of this success can be attributed to the development of deep learning
techniques such as convolutional neural networks (CNNs). While CNNs have pushed
the state-of-the-art forward, their training process requires a large amount of
clean and correctly labelled training data. If a CNN is intended to tolerate
facial pose, then we face an important question: should this training data be
diverse in its pose distribution, or should face images be normalized to a
single pose in a pre-processing step? To address this question, we evaluate a
number of popular facial landmarking and pose correction algorithms to
understand their effect on facial recognition performance. Additionally, we
introduce a new, automatic, single-image frontalization scheme that exceeds the
performance of current algorithms. CNNs trained using sets of different
pre-processing methods are used to extract features from the Point and Shoot
Challenge (PaSC) and CMU Multi-PIE datasets. We assert that the subsequent
verification and recognition performance serves to quantify the effectiveness
of each pose correction scheme.
