@inproceedings{97f6687d46684dabb458c9243f6d879f,
title = "Local variation joint representation for face recognition with single sample per person",
abstract = "Sparse representation based classification (SRC) was originally applied to multiple-training-sample face recognition with promising performance. Recently SRC has been extended to face recognition with single sample per person by using variations extracted from a generic training set as an additional common dictionary. However, the extended SRC ignored to learn a better variation dictionary and to use local region information of face images. To address this issue, we propose a local variation joint representation (LVJR) method, which learns a variation dictionary and does joint and local collaborative representation for a query image. The learned variation dictionary was required to do similar representation for the same-type facial variations, while the joint and local collaborative representation could effectively use local information of face images. Experiments on the large-scale CMU Multi-PIE and AR databases demonstrate that the proposed LVJR method achieves better results compared with the existing solutions to the single sample per person problem.",
keywords = "Face recognition, Joint representation, Local variation, Single sample per person",
author = "Meng Yang and Tiancheng Song and Shiqi Yu and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; 1st Chinese Conference on Computer Vision, CCCV 2015 ; Conference date: 18-09-2015 Through 20-09-2015",
year = "2015",
doi = "10.1007/978-3-662-48570-5_5",
language = "English",
isbn = "9783662485699",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "41--50",
editor = "Liang Wang and Hongbin Zha and Xilin Chen and Qiguang Miao",
booktitle = "Computer Vision CCF Chinese Conference, CCCV 2015, Proceedings",
address = "Germany",
}