The Behavior of Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) for Face Recognition

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    Abstract

    This paper presents the analysis of PCA-LDA behavior for face recognition using Singular Value Decomposition (SVD). The experimental results is shown to analyze face recognition performance, i.e. the impact of number of subjects, images per subject, training set size, and trade-off between the number of subjects and the number of images per subject on recognition performance, in relation with the number of PCA-LDA coefficients. The comparison of three classifiers, i.e. Euclidean Distance, Cosine Similarity, and Likelihood Ratio, are presented to obtain knowledge about their characteristics. All experimental evaluations are in the verification context. Based on the experimental results, the larger number of subjects and images per subject produced the better recognition performance. Regarding the number of subjects and images per subject trade-off, its indicated both of them influence the recognition performance. Otherwise, the image size also affect to recognition performance. PCA-LDA can perform low resolution image well up to 15x15 pixels and breaks down afterward. Regarding the p and ` coefficients, PCA-LDA has different behavior for each classifier.
    Original languageEnglish
    Title of host publicationProceedings of the 2018 Symposium on Information Theory and Signal Processing in the Benelux
    Subtitle of host publicationMay 31-1 June, 2018, University of Twente, Enschede, The Netherlands
    EditorsLuuk Spreeuwers, Jasper Goseling
    Place of PublicationEnschede
    PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
    Pages133-148
    Number of pages16
    ISBN (Print)978-90-365-4570-9
    Publication statusPublished - 31 May 2018
    Event39th Symposium on Information Theory and Signal Processing in the Benelux 2018 - University of Twente, Enschede, Netherlands
    Duration: 31 May 20181 Jun 2018
    Conference number: 39
    https://www.utwente.nl/en/eemcs/sitb2018/

    Conference

    Conference39th Symposium on Information Theory and Signal Processing in the Benelux 2018
    Abbreviated titleSITB
    CountryNetherlands
    CityEnschede
    Period31/05/181/06/18
    Internet address

    Keywords

    • Face recognition
    • Principal component analysis
    • Linear discriminant analysis

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  • Cite this

    Lestriandoko, N. H., Spreeuwers, L., & Veldhuis, R. (2018). The Behavior of Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) for Face Recognition. In L. Spreeuwers, & J. Goseling (Eds.), Proceedings of the 2018 Symposium on Information Theory and Signal Processing in the Benelux: May 31-1 June, 2018, University of Twente, Enschede, The Netherlands (pp. 133-148). Enschede: Werkgemeenschap voor Informatie- en Communicatietheorie (WIC).