On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches

Wong, Yongkang, Harandi, Mehrtash T., Sanderson, Conrad and Lovell, Brian C. (2012). On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches. In: Neural Networks (IJCNN), The 2012 International Joint Conference on. WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane Australia, (). 10-15 June 2012. doi:10.1109/IJCNN.2012.6252611

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Author Wong, Yongkang
Harandi, Mehrtash T.
Sanderson, Conrad
Lovell, Brian C.
Title of paper On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches
Conference name WCCI 2012 IEEE World Congress on Computational Intelligence
Conference location Brisbane Australia
Conference dates 10-15 June 2012
Proceedings title Neural Networks (IJCNN), The 2012 International Joint Conference on
Journal name International Joint Conference on Neural Networks. Proceedings
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/IJCNN.2012.6252611
Open Access Status Not yet assessed
ISBN 9781467314886
1467314889
ISSN 1098-7576
Total pages 8
Language eng
Formatted Abstract/Summary
In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the related literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image. Unfortunately, such assumption is easily violated in the more challenging and realistic face verification scenario. A verification algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person, while explicitly taking into account the possibility of impostor attacks. In this paper, we first discuss why most of the SR literature is not applicable to verification problems. Motivated by the success of bag-of-words methods in the field of object recognition, which describe an image as a set of local patches or interest points, we then propose to tackle the verification problem by encoding each local face patch through SR. The locally encoded sparse vectors are pooled to form regional descriptors, where each descriptor covers a relatively large portion of the face. Experiments in various challenging conditions show that the proposed method achieves high and robust verification performance.
Keyword Statistical-Models
Recognition
Representation
Database
Eigenfaces
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
 
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