Graduate School and Research Center in Digital Sciences

Semi-supervised face recognition with LDA self-training

Zhao, Xuran; Evans, Nicholas; Dugelay, Jean-Luc

ICIP 2011, IEEE International Conference on Image Processing, September 11-14, 2011, Brussels, Belgium

Face recognition algorithms based on linear discriminant analysis (LDA) generally give satisfactory performance but tend to require a relatively high number of samples in order to learn reliable projections. In many practical applications of face recognition there is only a small number of labelled face images and in this case LDA-based algorithms generally lead to poor performance. The contributions in this paper relate to a new semi-supervised, self-training LDA-based algorithm which is used to augment a manually labelled training set with new data from an unlabelled, auxiliary set and hence to improve recognition performance. Without the cost of manual labelling such auxiliary data is often easily acquired but is not normally useful for learning. We report face recognition experiments on 3 independent databases which demonstrate a constant improvement of our baseline, supervised LDA system. The performance of our algorithm is also shown to significantly outperform other semi-supervised learning algorithms.

Document Doi Bibtex

Title:Semi-supervised face recognition with LDA self-training
Keywords:face recognition, LDA, self-training, semi-supervised learning
Type:Conference
Language:English
City:Brussels
Country:BELGIUM
Date:
Department:Digital Security
Eurecom ref:3397
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Bibtex: @inproceedings{EURECOM+3397, doi = {http://dx.doi.org/10.1109/ICIP.2011.6116305 }, year = {2011}, title = {{S}emi-supervised face recognition with {LDA} self-training}, author = {{Z}hao, {X}uran and {E}vans, {N}icholas and {D}ugelay, {J}ean-{L}uc}, booktitle = {{ICIP} 2011, {IEEE} {I}nternational {C}onference on {I}mage {P}rocessing, {S}eptember 11-14, 2011, {B}russels, {B}elgium}, address = {{B}russels, {BELGIUM}}, month = {09}, url = {http://www.eurecom.fr/publication/3397} }
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