To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
This addresses the practical issue of improving face recognition accuracy for applications like security or identification, but it is incremental as it builds on existing pre-processing methods.
The paper tackles the problem of whether face recognition benefits from elaborate pre-processing like frontalization, evaluating various pose correction algorithms and introducing a new automatic frontalization scheme that outperforms current methods, with results quantified on PaSC and CMU Multi-PIE datasets.
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.