Contourlet and nearest feature line based feature extraction approach for one prototype sample problem

Jeng Shyang Pan, Lijun Yan, Shu Chuan Chu, John F. Roddick

    Research output: Contribution to journalArticle

    Abstract

    In this paper, a novel algorithm for face recognition with one sample per person is proposed. The proposed algorithm is based on contourlet transformation. For simple prototype sample problem, many discriminant learning methods can not work. Because for most discriminant learning methods, the within class scatter of the prototype samples are very important. However, simple prototype sample problem does not have within class scatter. To enhance the representative capability of the prototype samples set, some new samples are generated using contourlet transformation. Multiple prototype samples for each class are constructed through the decomposition and reconstruction of original training images by contourlet transformation. Thus neighborhood discriminant nearest feature line analysis can be performed on the new database. The experimental results demonstrate the efficiency of the proposed algorithm.

    Original languageEnglish
    Pages (from-to)1052-1059
    Number of pages8
    JournalJournal of Information Hiding and Multimedia Signal Processing
    Volume7
    Issue number5
    Publication statusPublished - Sep 2016

    Keywords

    • Contourlet
    • Image classification
    • Neighborhood discriminant nearest feature line analysis

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