We present a fusion of Gabor feature based Support Vector Machine (SVM) classifiers for face verification. 40 wavelets are used in parallel to extract features for face representation. These 40 feature extracted vectors are first projected onto the corresponding Principal Component Analysis (PCA) subspaces, and then fed into 40 SVMs for classification and fusion. No downsample is used. A publicly available FRAV2D face database with 4 different kinds of tests, each with 4 images per person, has been used to test our algorithm, considering frontal views, images with gestures, occlusions and changes of illumination. Compared to three baseline methods developed in literature, i.e. PCA, feature-based Gabor PCA and downsampled Gabor PCA, the proposed algorithm achieved the best results in the neutral expression and occlusion experiments. Compared to a downsampled Gabor PCA method, our algorithm also obtained similar error rates with a lower feature dimension.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fusion of Gabor Feature Based Classifiers for Face Verification


    Contributors:


    Publication date :

    2007-09-01


    Size :

    2951169 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Gabor Feature Classifier for Face Recognition

    Liu, C. / Wechsler, H. / IEEE | British Library Conference Proceedings | 2001


    A Gabor feature classifier for face recognition

    Chengjun Liu, / Wechsler, H. | IEEE | 2001


    Discriminant analysis with Gabor phase feature for robust face recognition

    Han, H. / Zhu, J. / Lei, Z. et al. | British Library Online Contents | 2013


    Iris Recognition Method Using Log-Gabor Filtering and Feature Fusion

    Fenghua, W. / Jiuqiang, H. | British Library Online Contents | 2007