| dc.description.abstract | Face recognition technology is one of the advanced technologies that help to recognize and identify human faces using an image or video clip. Although many face recognition techniques have been proposed in the literature, a robust face recognition system is still a challenging task. It is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this paper, a new set of training samples is generated from the original samples, using the symmetry property of the face and the recognition performance is improved. The proposed method has three main stages: generating new images, feature extraction and classification. the symmetry property of the face is used to generate new images, the Histograms of Oriented Gradients is used for feature extraction and the Euclidean distance is used for classification. The proposed method is tested and evaluated using AR dataset which is widely used for testing and comparing the accuracy of face recognition systems. The experimental results show that the proposed method has a recognition accuracy rates higher than the traditional methods. | en_US |
