Face recognition improvement with distortions of images in training set
Ernst M. Kussul, Tetyana Baydyk, Cristina Conde, Isaac Martín de Diego, Enrique Cabello · 2013
The results and comparative analysis of two face recognition methods are presented in this article. The Permutation Coding Neural Classifier (PCNC) and Support Vector Machine (SVM) methods were selected. The main idea is to improve the image recognition rate. For this purpose we increase the training set by including distortions of initial images. Different numbers of distortions can increase the training image set and improve the quality of the classifier. The goal is to investigate the influence of the number of distortions on the PCNC recognition rate. Using distortions it is possible to improve the PCNC recognition rate. Sometimes it is possible to decrease the number of errors by 10 times or more. The PCNC with 12 distortions outperforms the results of SVM.