In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support Vector Machine (SVM). In addition, these classification methods are applied on raw images to gauge the performance of these dimensionality reduction techniques. All experiments have been performed on images from UMIST Face Database.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An experimental evaluation of linear and kernel-based methods for face recognition


    Contributors:
    Gupta, H. (author) / Agrawal, A.K. (author) / Pruthi, T. (author) / Shekhar, C. (author) / Chellappa, R. (author)


    Publication date :

    2002-01-01


    Size :

    977912 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition

    Gupta, H. / Agrawal, A. K. / Pruthi, T. et al. | British Library Conference Proceedings | 2002





    Kernel Discriminant Analysis Based on Canonical Differences for Face Recognition in Image Sets

    Chu, Wen-Sheng Vincnent / Chen, Ju-Chin / Lien, Jenn-Jier James | Springer Verlag | 2007