Regularization and attention feature distillation base on light CNN for Hyperspectral face recognition

Abstract

The hyperspectral imaging, capturing discriminative information across a series of spectrum bands, leads to building a robust face recognition system. Motivated by the success of deep convolutional network and transferring learning, this paper proposed an end-to-end hyperspectral face recognition model based on a light Convolutional Neural Network (CNN) and transfer learning. To boost the performance of hyperspectral face recognition, the Max-Feature-Map (MFM) activation function and fine-tuning (structure regularization(L2SP) or attention feature distillation (AFD)) are introduced to optimize the deep network, which will learn the fine feature representation across different bands. Especially, this method incorporates regularization and AFD cooperation into the transfer learning strategy on the visible face data. By feeding back hyperspectral images to the pretrained light CNN network, we can design an end-to end model that can leverage the generalization ability for hyperspectral face images. Finally, the entire model is trained and verified on the PolyU-HSFD, CMU, and UWA hyperspectral face datasets using the associated standard evaluation protocols. Experimental results demonstrate that the improved Light CNN network can get good representations of hyperspectral face features and the joint training with a combination of L2SP and AFD exhibits better recognition performance than the state-of-the-art methods based on other deep networks.