Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm

Abstract

Abstract. 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%.

Face Recognition based Texture Analysis Methods

“…The performance mensuration of proposed system is evaluated using three important computations: False Rejection Rate (FRR), False Acceptance Rate (FAR), and accuracy rate as explained in (7), (8), and (9). Table 1 explains the results of FAR, FRR, and accuracy rates using LBP and GLCM method; the maximum accuracy rate has been satisfied with Euclidean distance classifier for LBP method, while the Euclidean and Cosine distance achieved maximum accuracy rate with GLCM method.…”

“…Particle Swarm Optimization (PSO) method was used for selecting the Support Vector Machine's (SVM) parameters that utilized for classification phase. This proposed method acquired 98% accuracy rate using ORL databases [8].…”

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

“…In this work, similar to others, Xiao et al (2014) used PSO combined with grid-search to optimize the parameters of a Radial Basis Function (RBF) kernel in SVM. Similarly, Zou and Zhang (2016) presented their PO work using the recognition rate to calculate the tness of each particle. The above mentioned works in the present paragraph used mostly the following databases: ORL, Color FERET, Yale A and BioID.…”