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Face Recognition in real-world images

Citation Author(s):
Xavier Fontaine, Radhakrishna Achanta, Sabine Süsstrunk
Submitted by:
Xavier Fontaine
Last updated:
1 April 2017 - 11:50pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Xavier Fontaine
Paper Code:
1926
 

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.

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