A further improved support vector machine model along with particle swarm optimization for face orientations recognition based on eigeneyes by using hybrid kernel
Y. Liu, Yaning Shi, Mayi Xu, L. L. Zhang, Nan Yu, Yong Ding · 2017
The learning vector quantization (LVQ), back propagation neural network (BPNN), and support vector machine (SVM) models were established to recognize face orientations. A precision function (P) was proposed to compute each model's precision with confusion matrix. The aforehand models were improved by intelligent algorithms to become LVQ with K-fold cross validation (CV-LVQ) model, BPNN with GA (GA-BPNN) model, and SVM with particle swarm optimization (PSO-SVM) model. The kernel function in the PSO-SVM model was assumed to RBF kernel which had relatively weaker learning ability. Hence the PSO-SVM model was further improved with a hybrid kernel that was fused with the generalization performance of global kernel and the learning ability of local kernel. The further improved PSO-SVM (IPSO-SVM) model possessed a 1.63 to 9.25 percent higher precision than PSO-SVM model. There were no obvious differences in the average elapsed time (AET) between IPSO-SVM model and PSO-SVM model. The results show that IPSO-SVM model not only reaches an outstanding precision of 98.14%, but also was practicable for the recognition of face orientations.