Deep Sparse Band Selection for Hyperspectral Face Recognition

TL;DR

This paper introduces a CNN-based method with structural sparsity learning to automatically select optimal hyperspectral bands, significantly improving face recognition performance over existing methods.

Contribution

It proposes a novel CNN framework with group Lasso regularization for automatic spectral band selection in hyperspectral face recognition.

Findings

Outperforms state-of-the-art band selection methods

Automatically selects the most relevant spectral bands

Achieves higher recognition accuracy on public datasets

Abstract

Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spectrum has been exploited to improve face recognition performance over all the conventional broad band face images. In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands. Specifically, in this method, images from all bands are fed to a CNN, and the convolutional filters in the first layer of the CNN are then regularized by employing a group Lasso algorithm to zero out the redundant bands during the training of the network. Contrary to other methods which usually select the useful bands manually or in a greedy fashion, our method selects the optimal spectral bands automatically to achieve the best face recognition performance over all spectral bands. Moreover, experimental results demonstrate that our method outperforms state of the art band selection methods for face recognition on several publicly-available hyperspectral face image datasets.