Shared feature extraction for nearest neighbor face recognition

David Masip, Jordi Vitrià

    Research output: Contribution to journalArticleResearchpeer-review

    19 Citations (Scopus)

    Abstract

    In this paper, we propose a new supervised linear feature extraction technique for multiclass classification problems that is specially suited to the nearest neighbor classifier (NN). The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. This strategy allows the introduction of a multitask learning (MTL) criterion in the method and results in a solution that makes no assumptions about the data distribution and that is specially appropriated to solve the small sample size problem. The performance of the method is illustrated by an application to the face recognition problem. The experiments show that the representation obtained following the multitask approach improves the classic feature extraction algorithms when using the NN classifier, especially when we have a few examples from each class. © 2008 IEEE.
    Original languageEnglish
    Pages (from-to)586-595
    JournalIEEE Transactions on Neural Networks
    Volume19
    Issue number4
    DOIs
    Publication statusPublished - 1 Apr 2008

    Keywords

    • Face recognition
    • Feature extraction
    • Multitask learning (MTL)
    • Nearest neighbor classification (NN)
    • Small sample size problem

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