2014 IEEE International Conference on Multimedia and Expo (ICME) (2014)
July 14, 2014 to July 18, 2014
Chi Zhang , Beijing University of Posts and Telecommunications
Xiang Sun , Tsinghua University
Jiani Hu , Beijing University of Posts and Telecommunications
Weihong Deng , Beijing University of Posts and Telecommunications
Recently, discriminative methods such as SVM has been widely used in object location. But there has been no method to perform well enough both at accuracy and speed. For linear SVM, it is hard to separate the nonlinear samples exactly. For kernel SVM, it is hard to be applied to real-time application, because of the computational cost kernel function. Local linear SVM has been proved to be a good tradeoff between fast linear SVM and qualitative best kernel methods. However, it is still time-consuming for real-time application. To design a high efficiency and high precision eye locahzer, first, we deduce a fast variation for LL-S VM which can serve as a more fast and accurate substitute of the traditional nonlinear kernel SVM. Second, to further improve the speed, we also adopt a candidate selection strategy. Extensive experiments on the BioID, FERET, FRGC, and LFW database show that our proposed method achieves favorable localization accuracy against other state-of-the-art methods at a speed as fast as 5ms to localize two eyes.
Decision support systems,
C. Zhang, X. Sun, J. Hu and W. Deng, "Precise eye localization by fast local linear SVM," 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China, 2014, pp. 1-6.