Paper Info
Title
To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?
Abstract
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 facial landmarking algorithms and a popular frontalization method to understand their effect on facial recognition performance. Additionally, we introduce a new, automatic, single-image frontalization scheme that exceeds the performance of the reference frontalization algorithm for video-to-video face matching on the Point and Shoot Challenge (PaSC) dataset. Additionally, we investigate failure modes of each frontalization method on different facial yaw using the CMU Multi-PIE dataset. We assert that the subsequent recognition and verification performance serves to quantify the effectiveness of each pose correction scheme.
Year
DOI
Venue
2018
10.1109/wacv.2018.00009
WACV
Field
DocType
Citations 
Training set,Facial recognition system,Correction algorithm,Normalization (statistics),Pattern recognition,Computer science,Convolutional neural network,Artificial intelligence,Deep learning,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
31
8
Name
Order
Citations
PageRank
Sandipan Banerjee1174.52
Joel Brogan201.35
Janez Križaj3395.27
Aparna Bharati4304.56
Brandon RichardWebster501.01
Vitomir Struc623626.44
Patrick J. Flynn730820.13
Walter J. Scheirer877352.81