Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm
Dan Zou, Hong Zhang
Available Online December 2016.
- https://doi.org/10.2991/mcei-16.2016.81How to use a DOI?
- Wavelet Transform; Two Dimensional Linear Discriminant Analysis; Particle Swarm Optimization; Support Vector Machine; Face Recognition
- Concerning the "Small Samples Size" problem in LDA algorithm and reduce the effects to the SVM face recognition rate caused by random parameters set by human. An algorithm based on combination with the PSO algorithm which was originated form artificial life and evolutionary computation to SVM's parameters election and optimization, and Wavelet Transform , two-dimensional LDA(2DLDA) was proposed. Firstly, the original images were decomposed into high-frequency and low-frequency Components by Wavelet Transform (WT). The high-frequency components were ignored, while the low-frequency components can be obtained. Then, the liner discriminant features were extracted by two-dimensional LDA (2DLDA). Finally, we use the PSO algorithm to SVM's parameters election and optimization. Experimental results based on ORL face database show the validity of the algorithm this paper proposed for face recognition and it can reach the recognition rate of 98%.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Dan Zou AU - Hong Zhang PY - 2016/12 DA - 2016/12 TI - Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm BT - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016) PB - Atlantis Press SP - 391 EP - 399 SN - 1951-6851 UR - https://doi.org/10.2991/mcei-16.2016.81 DO - https://doi.org/10.2991/mcei-16.2016.81 ID - Zou2016/12 ER -