An application of KPCA and SVM in the human face recognition

Face Recognition technology is a very important biological feature Recognition technology. Face Recognition is more and more researchers' attention, especially the principal Component Analysis method (Principle Component Analysis, PCA) after the application of Face Recognition, Face Recognition application domain expands unceasingly in daily life, such as immigration, entrance guard system, the Olympic security, airport security checks, etc. Although the face recognition system has better recognition effect has been achieved, but still by illumination, posture, facial expression change, hairstyle, with or without glasses, and the influence of various factors such as aging. Therefore, in this paper, the study of face recognition technology, has important theory significance and practical application value.A face recognition method that based on KPCA and SVM is proposed in this paper.

[1]  Wei Wang,et al.  Face recognition algorithm using wavelet decomposition and Support Vector Machines , 2012, 2012 International Symposium on Optomechatronic Technologies (ISOT 2012).

[2]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[3]  Xin Chen,et al.  PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies , 2003, AMFG.

[4]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[5]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[6]  Jianming Jin,et al.  Mathematical formulas extraction , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[7]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[9]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[10]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[11]  Changle Zhou,et al.  Face recognition using support vector machines with the robust feature , 2003, The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003..

[12]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Fuji Ren,et al.  Detect and track the dynamic deformation human body with the active shape model modified by motion vectors , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.