Hebei Key Laboratory of Data Science and Applications, North China University of Science and Technology
College of Grassland and Environment Sciences, Xinjiang Agricultural University
Ministry of Education, Key Laboratory with Modern Metallurgical Technology, North China University of Science and Technology
: Received: 11 October 2016 / Approved: 11 October 2016 / Online: 11 October 2016 (14:42:02 CEST)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite:
Yang, A.; Han, Y.; Li, J.; Pan, Y.; Shen, L.; Long, Y.; Zhang, Y. Research on an SVM Coupling Algorithm of Image Recognition. Preprints2016, 2016100038 (doi: 10.20944/preprints201610.0038.v1).
Yang, A.; Han, Y.; Li, J.; Pan, Y.; Shen, L.; Long, Y.; Zhang, Y. Research on an SVM Coupling Algorithm of Image Recognition. Preprints 2016, 2016100038 (doi: 10.20944/preprints201610.0038.v1).
The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.
Face Recognition; Intelligent Coupling Algorithm; Robustnes; Accuracy; Speed