Mongolia Nationality Face Recognition Based on G(2D)2PCA and SVM Classification

Aiming at the realization of national feature extraction, an improved face recognition method is proposed in order to preserve some essential local features. Gabor feature-based on bi-directional two-dimensional principal component analysis ((2D)2PCA) is applied for face images. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. Experimental results on Mongolia Nationality face database showed that the proposed method achieved better recognition accuracy compared with several other face recognition algorithms.