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Centre for Pattern Recognition and Data Analytics
School of Information Technology
Locked Bag 20000
GEELONG VIC 3220
T. Tran, D.Q. Phung, and S. Venkatesh. Learning Boltzmann distance metric for face recognition. In Proc. of IEEE International Conference on Multimedia & Expo (ICME), Melbourne, Australia, 2012.
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 informationtheoretic 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 whole-face pixels to 99.2 feature extractors and appropriate resolutions.
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19th February 2015