Improving Face Recognition via Narrowband Spectral Range Selection Using Jeffrey Divergence

In order to achieve improved recognition performance in comparison with conventional broadband images, this paper addresses a new method that automatically specifies the optimal spectral range for multispectral face images according to given illuminations. The novelty of our method lies in the introduction of a distribution separation measure and the selection of the optimal spectral range by ranking these separation values. The selected spectral ranges are consistent with the physics analysis of the multispectral imaging process. The fused images from these chosen spectral ranges are verified to outperform the conventional broadband images by 3%-20%, based on a variety of experiments with indoor and outdoor illuminations using two well-recognized face-recognition engines. Our discovery can be practically used for a new customized sensor design associated with given illuminations for improved face-recognition performance over the conventional broadband images.