FDLDA: An Fast Direct LDA Algorithm For Face Recognition
Zhibo Guo, Kejun Lin, Yunyang Yan
Available Online August 2016.
- https://doi.org/10.2991/cset-16.2016.78How to use a DOI?
- Feature extraction, fast direct linear discriminant analysis, face recogniation
- Feature extraction is one of the hot topics in face recognition. However, many face extraction methods will suffer from the "small sample size" problem, such as Linear Discriminant Analysis (LDA). Direct Linear Discriminant Analysis (DLDA) is an effective method to address this problem. But conventional DLDA algorithm is often computationally expensive and not scalable. In this paper, DLDA is analyzed from a new viewpoint via SVD and an fast and robust method named FDLDA algorithm is proposed. The proposed algorithm achieves high efficiency by introducing the SVD on a small-size matrix, while keeping competitive classification accuracy. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhibo Guo AU - Kejun Lin AU - Yunyang Yan PY - 2016/08 DA - 2016/08 TI - FDLDA: An Fast Direct LDA Algorithm For Face Recognition PB - Atlantis Press SP - 334 EP - 337 SN - 2352-538X UR - https://doi.org/10.2991/cset-16.2016.78 DO - https://doi.org/10.2991/cset-16.2016.78 ID - Guo2016/08 ER -