Face Recognition in Real-world Images

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training examples and is computationally efficient. Our method consists of performing a novel alignment process followed by classification using sparse representation techniques. We present our recognition rates on a difficult dataset that represents real-world faces where we significantly outperform state-of-the-art methods.


Published in:
2017 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 1482-1486
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March 5-9
Year:
2017
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-5090-4117-6
Keywords:
Laboratories:




 Record created 2017-01-10, last modified 2018-03-17

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