This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET  and MUCT , and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.
|Number of pages||9|
|Journal||WSEAS Transactions on Systems and Control|
|State||Published - 2020|
Bibliographical notePublisher Copyright:
© 2020, World Scientific and Engineering Academy and Society. All rights reserved.
- 2D images
- Databases feret
- Databases muct
- Facial recognition
- Gabor wavelet
- Support vector machines