@inproceedings{Michael2016,
abstract = {In this work we present a general framework for robust error estimation in face recognition. The proposed formulation allows the simultaneous use of various loss functions for modeling the residual in face images, which usually follows non-standard distributions, depending on the image capturing conditions. Our method extends the current vast literature offering flexibility in the selection of the residual modeling characteristics but, at the same time, considering many existing algorithms as special cases. As such, it proves robust for a range of error inducing factors, such as, varying illumination, occlusion, pixel corruption, disguise or their combinations. Extensive simulations document the superiority of selecting multiple models for representing the noise term in face recognition problems, allowing the algorithm to achieve near-optimal performance in most of the tested face databases. Finally, the multi-model residual representation offers useful insights into understanding how different noise types affect face recognition rates.},
author = {Iliadis, Michael and Spinoulas, Leonidas and Berahas, Albert S. and Wang, Haohong and Katsaggelos, Aggelos K.},
booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2016.7532956},
isbn = {978-1-4673-9961-6},
issn = {15224880},
keywords = {Error correction,Face recognition,Robust representation,Sparse representation},
month = {sep},
pages = {3229--3233},
publisher = {IEEE},
title = {{Multi-model robust error correction for face recognition}},
url = {http://ieeexplore.ieee.org/document/7532956/},
volume = {2016-Augus},
year = {2016}
}

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