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Prototype-based class-specific nonlinear subspace learning for large-scale face verification

Tutkimustuotosvertaisarvioitu

Standard

Prototype-based class-specific nonlinear subspace learning for large-scale face verification. / Iosifidis, Alexandros; Gabbouj, Moncef.

2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. s. 1-6.

Tutkimustuotosvertaisarvioitu

Harvard

Iosifidis, A & Gabbouj, M 2016, Prototype-based class-specific nonlinear subspace learning for large-scale face verification. julkaisussa 2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, Sivut 1-6, International Conference on Image Processing Theory, Tools and Applications, 1/01/00. https://doi.org/10.1109/IPTA.2016.7820988

APA

Iosifidis, A., & Gabbouj, M. (2016). Prototype-based class-specific nonlinear subspace learning for large-scale face verification. teoksessa 2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA) (Sivut 1-6). IEEE. https://doi.org/10.1109/IPTA.2016.7820988

Vancouver

Iosifidis A, Gabbouj M. Prototype-based class-specific nonlinear subspace learning for large-scale face verification. julkaisussa 2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE. 2016. s. 1-6 https://doi.org/10.1109/IPTA.2016.7820988

Author

Iosifidis, Alexandros ; Gabbouj, Moncef. / Prototype-based class-specific nonlinear subspace learning for large-scale face verification. 2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. Sivut 1-6

Bibtex - Lataa

@inproceedings{dbae0b879ce3418cbe73a1b00403a0b6,
title = "Prototype-based class-specific nonlinear subspace learning for large-scale face verification",
abstract = "In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show the effectiveness of the proposed approach, since it scales well with the data size and outperforms related approaches.",
keywords = "Face, Face recognition, Kernel, Optimization, Prototypes, Training, Training data, Class-Specific Discriminant Analysis, Nonlinear Subspace Learning, Prototype-based Approximation",
author = "Alexandros Iosifidis and Moncef Gabbouj",
year = "2016",
month = "12",
doi = "10.1109/IPTA.2016.7820988",
language = "English",
isbn = "978-1-4673-8911-2",
publisher = "IEEE",
pages = "1--6",
booktitle = "2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Prototype-based class-specific nonlinear subspace learning for large-scale face verification

AU - Iosifidis, Alexandros

AU - Gabbouj, Moncef

PY - 2016/12

Y1 - 2016/12

N2 - In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show the effectiveness of the proposed approach, since it scales well with the data size and outperforms related approaches.

AB - In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show the effectiveness of the proposed approach, since it scales well with the data size and outperforms related approaches.

KW - Face

KW - Face recognition

KW - Kernel

KW - Optimization

KW - Prototypes

KW - Training

KW - Training data

KW - Class-Specific Discriminant Analysis

KW - Nonlinear Subspace Learning

KW - Prototype-based Approximation

U2 - 10.1109/IPTA.2016.7820988

DO - 10.1109/IPTA.2016.7820988

M3 - Conference contribution

SN - 978-1-4673-8911-2

SP - 1

EP - 6

BT - 2016 6th International Conference on Image Processing Theory, Tools and Applications (IPTA)

PB - IEEE

ER -