Single sample per person face recognition with KPCANet and a weighted voting scheme

Most current methods of facial recognition rely on the condition of having multiple samples per person available for feature extraction. In practical applications, however, only one sample may be available for each person to train a model with. As a result, many of the traditional methods fall short, leaving the challenge of facial recognition greater than ever. To deal with this challenge, this study addresses a face recognition algorithm based on a kernel principal component analysis network (KPCANet) and then proposes a weighted voting method. First, the aligned face image is partitioned into several non-overlapping patches to form the training set. Next, a KPCANet is used to obtain filters and feature banks. Finally, the identification of the unlabeled probe occurs through the application of the weighted voting method. Based on several widely used face datasets, the results of the experiments demonstrate the superiority of the proposed method.