Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition
Xiwei Dong, Fei Wu, Xiao‐Yuan Jing · KSII Transactions on Internet and Information Systems · 2018
Face recognition (FR) with a single sample per person (SSPP) is common in real-world face recognition applications.In this scenario, it is hard to predict intra-class variations of query samples by gallery samples due to the lack of sufficient training samples.Inspired by the fact that similar faces have similar intra-class variations, we propose a virtual sample generating algorithm called k nearest neighbors based virtual sample generating (kNNVSG) to enrich intra-class variation information for training samples.Furthermore, in order to use the intra-class variation information of the virtual samples generated by kNNVSG algorithm, we propose image set based multimanifold discriminant learning (ISMMDL) algorithm.For ISMMDL algorithm, it learns a projection matrix for each manifold modeled by the local patches of the images of each class, which aims to minimize the margins of intra-manifold and maximize the margins of inter-manifold simultaneously in low-dimensional feature space.Finally, by comprehensively using kNNVSG and ISMMDL algorithms, we propose k nearest neighbor virtual image set based multimanifold discriminant learning (kNNMMDL) approach for single sample face recognition (SSFR) tasks.Experimental results on AR, Multi-PIE and LFW face datasets demonstrate that our approach has promising abilities for SSFR with expression, illumination and disguise variations.