Supervised Feature Learning Network Based on the Improved LLE for face recognition

Abstract

Deep neural networks (DNNs) have been successfully applied in the fields of computer vision and pattern recognition. One drawback of DNNs is that most of existing DNNs models and their variants usually need to learn a very large set of parameters. Another drawback of DNNs is that DNNs does not fully take the class label and local structure into account during the training stage. To address these issues, this paper proposes a novel approach, called Supervised Feature Learning Network Based on the Improved LLE (SFLNet) for face recognition. The goal of SFLNet is to extract features efficiently. Thus SFLNet consists of learning kernels based on the improved Locally Linear Embedding (LLE) and multiscale feature analysis. Instead of taking image pixels as the input of LLE algorithm, the improved LLE uses linear discriminant kernel distance (LDKD). Besides, the outputs of the improved LLE are convolutional kernels, not the dimensional reduction features. Mutiscale feature analysis enhances the insensitive to complex changes caused by large pose, expression, or illumination variations. So SFLNet has better discrimination and is more suitable for face recognition task. Experimental results on Extended Yale B and AR dataset shows the impressive improvement of the proposed method and robustness to occlusion when compared with other state-of-art methods.