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Learning Boltzmann distance metric for face recognition Tran, Truyen, Phung, Dinh Q. and Venkatesh, Svetha 2012, Learning Boltzmann distance metric for face recognition, in ICME 2012 : Proceedings of the 13th IEEE International Conference on Multimedia and Expo, IEEE, Piscataway, N.J., pp. 218-223. Attached Files Name Description MIMEType Size Downloads Title Learning Boltzmann distance metric for face recognition Author(s) Tran, Truyen Phung, Dinh Q. Venkatesh, Svetha Conference name IEEE Multimedia and Expo Conference (13th : 2012 : Melbourne, Victoria) Conference location Melbourne, Victoria Conference dates 9-13 Jul. 2012 Title of proceedings ICME 2012 : Proceedings of the 13th IEEE International Conference on Multimedia and Expo Editor(s) [Unknown] Publication date 2012 Conference series IEEE Multimedia and Expo Conference Start page 218 End page 223 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) face recognition information fusion metric learning Restricted Boltzmann Machines Summary We introduce a new method for face recognition using a versatile probabilistic model known as Restricted Boltzmann Machine (RBM). In particular, we propose to regularise the standard data likelihood learning with an information-theoretic distance metric defined on intra-personal images. This results in an effective face representation which captures the regularities in the face space and minimises the intra-personal variations. In addition, our method allows easy incorporation of multiple feature sets with controllable level of sparsity. Our experiments on a high variation dataset show that the proposed method is competitive against other metric learning rivals. We also investigated the RBM method under a variety of settings, including fusing facial parts and utilising localised feature detectors under varying resolutions. In particular, the accuracy is boosted from 71.8% with the standard whole-face pixels to 99.2% with combination of facial parts, localised feature extractors and appropriate resolutions. ISBN 9780769547114 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30051374 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Mar 2013, 09:43:26 EST Mon, 18 Mar 2013, 10:15:13 EST Wed, 08 May 2013, 15:33:43 EST Thu, 09 May 2013, 08:31:45 EST Mon, 27 May 2013, 13:56:07 EST Filtered Full Access Statistics: 24 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 18 Mar 2013, 09:43:26 EST

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