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Natural Computing

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01-09-2012

Towards collaborative feature extraction for face recognition

Authors: Eduardo Rodriguez, Konstantinos Nikolaidis, Tingting Mu, Jason F. Ralph, John Y. Goulermas

Published in: Natural Computing | Issue 3/2012

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Abstract

Principal components analysis has become a popular preprocessing method to avoid the small sample size problem for most of the supervised graph embedding methods. Nevertheless, there is potential loss of relevant information when projecting the data onto the space defined by the principal Eigenfaces when the number of individuals in the gallery is large. This paper introduces a new collaborative feature extraction method based on projection pursuit, as a robust preprocessing for supervised embedding methods. A previously proposed projection index was adopted as a measure of interestingness, based on a weighted sum of six state of the art indices. We compare our collaborative feature extraction technique against principal component analysis as preprocessing stage for Laplacianfaces. For completeness, results for Eigenfaces and Fisherfaces are included. Experimental results to demonstrate the robustness of our approach against changes in facial expression and lighting are presented.

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Title: Towards collaborative feature extraction for face recognition
Authors: Eduardo Rodriguez
Konstantinos Nikolaidis
Tingting Mu
Jason F. Ralph
John Y. Goulermas
Publication date: 01-09-2012
Publisher: Springer Netherlands
Published in: Natural Computing / Issue 3/2012
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-011-9285-6

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