Face recognition based on Volterra kernels direct discriminant analysis and effective feature classification

We present a novel face recognition method based on direct discriminant Volterra kernels and effective feature classification (DD-VK). One of the crucial steps involves dividing face images into patches and using the DD-VK method to extract the features of sub-image patches. DD-VK implements diagonalization to discard useless information in the null space of the inter-class scatter matrix and preserve important discriminant information in the null space of the intra-class scatter matrix. This method can simultaneously maximize inter-class distances and minimize intra-class distances in the feature space. We also introduce a novel classification scheme associated with the 2D Volterra kernel feature. Our scheme aggregates the classification information obtained from each column of the feature matrix in each image patch and uses a voting strategy to implement parent face image classification. This procedure can reduce the influence of local negative information. Experimental results show that the proposed method demonstrates good performance when dealing with conventional face recognition problems and exhibits strong robustness when dealing with block occlusion images.