Subspace learning for face recognition

Our research focuses on learning the low-dimensional embeddings of face images.

Our research focuses on learning the low-dimensional embeddings of face images.

Subspace learning for computer vision applications has recently generated a significant amount of scientific research. This research has been primarily motivated by the development of a multitude of techniques for the efficient analysis of high-dimensional data via non-linear dimensionality reduction.

These techniques have provided valuable tools for understanding and capturing the intrinsic non-linear structure of visual data encountered in many important machine vision problems. At the same time, there has been a substantially increasing interest in related applications such as appearance-based object/face recognition.

The group's research concentrates on robust kernel-based extensions to linear subspace analysis as well as manifold learning techniques.

Visualization of FreyFaces using Locally Linear Embedding (left figure) and our learning algorithm (right figure)

Involved group members

Maja Pantic, Baris Gecer, Athanasios Papaioannou, Georgios Tzimiropoulos, Yujiang Wang

Related Publications

  1. Subspace Learning from Image Gradient Orientations

    G. Tzimiropoulos, S. Zafeiriou, M. Pantic. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(12): pp. 2454 - 2466, 2012.

    [Resources (Code)] [pdf] [bibtex] [endnote]
    Bibtex reference [hide]
    @article{tzimiro2012,
        author = {G. Tzimiropoulos and S. Zafeiriou and M. Pantic},
        pages = {2454--2466},
        journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
        number = {12},
        title = {Subspace Learning from Image Gradient Orientations},
        volume = {34},
        year = {2012},
    }
    Endnote reference [hide]
    %0 Journal Article
    %T Subspace Learning from Image Gradient Orientations
    %A Tzimiropoulos, G.
    %A Zafeiriou, S.
    %A Pantic, M.
    %J IEEE Transactions on Pattern Analysis and Machine Intelligence
    %D 2012
    %V 34
    %N 12
    %F tzimiro2012
    %P 2454-2466

  2. Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification

    S. Zafeiriou, G. Tzimiropoulos, M. Petrou, T. Stathaki. IEEE Transactions on Neural Networks and Learning Systems (accepted for publication). 2011.

    [pdf] [bibtex] [endnote]
    Bibtex reference [hide]
    @article{szafeirioutnn2011,
        author = {S. Zafeiriou and G. Tzimiropoulos and M. Petrou and T. Stathaki},
        journal = {IEEE Transactions on Neural Networks and Learning Systems (accepted for publication)},
        title = {Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification},
        year = {2011},
    }
    Endnote reference [hide]
    %0 Journal Article
    %T Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification
    %A Zafeiriou, S.
    %A Tzimiropoulos, G.
    %A Petrou, M.
    %A Stathaki, T.
    %J IEEE Transactions on Neural Networks and Learning Systems (accepted for publication)
    %D 2011
    %F szafeirioutnn2011

  3. Principal Component Analysis of Image Gradient Orientations for Face Recognition

    G. Tzimiropoulos, S. Zafeiriou, M. Pantic. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FG'11). Santa Barbara, CA, USA, pp. 553 - 558, March 2011.

    [pdf] [bibtex] [endnote]
    Bibtex reference [hide]
    @inproceedings{tzimiropoulosEtAlFG2011,
        author = {G. Tzimiropoulos and S. Zafeiriou and M. Pantic},
        pages = {553--558},
        address = {Santa Barbara, CA, USA},
        booktitle = {Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FG'11)},
        month = {March},
        title = {Principal Component Analysis of Image Gradient Orientations for Face Recognition},
        year = {2011},
    }
    Endnote reference [hide]
    %0 Conference Proceedings
    %T Principal Component Analysis of Image Gradient Orientations for Face Recognition
    %A Tzimiropoulos, G.
    %A Zafeiriou, S.
    %A Pantic, M.
    %B Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FG?11)
    %D 2011
    %8 March
    %C Santa Barbara, CA, USA
    %F tzimiropoulosEtAlFG2011
    %P 553-558