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, doi: 10.1109/ICME.2012.131.
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Title
Learning Boltzmann distance metric for face recognition
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
DOI
10.1109/ICME.2012.131
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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