Self-adjusting multilayer nonlinear coupled mapping for low-resolution face recognition

Abstract

In practical surveillance scenes, effective recognition of low-resolution (LR) images is an urgent issue. At present, the improvement of methods for LR image recognition tasks mainly focuses on how to effectively extract information from LR images. This paper explores a novel self-adjusting multilayer nonlinear coupled mapping (SMNCM) network. The proposed SMNCM includes two main components, the multilayer nonlinear coupled mapping (MNCM) module, which is the feature extractor, and the self-adjusting feature fusion (SaFF) module. In the MNCM module, the LR template image set and multiple different high-resolution template image sets are used to construct a pyramid-like multicoupled mapping feature extraction network. In the SaFF module, the fusion weights of multiple features and the other parameters are collaboratively learned for the purpose of correct classification by the gradient learning network. To achieve this goal, a new gradient optimization model for fusion weights and other parameters is constructed. The features fused by the learned weights and the multiple features of the MNCM module are more suitable for classification tasks. Experimental results indicate that on the five databases, our proposed method outperforms the state-of-the-art approaches for LR face recognition. The ablation experiment suggests that each module in the proposed method can contribute to improving the LR image recognition performance.