Near-IR to visible light face matching: Effectiveness of pre-processing options for commercial matchers

Abstract

The use of near-IR images for face recognition has been proposed as a means to address illumination issues that can hinder standard visible light face matching. However, most existing non-experimental databases contain visible light images. This makes the matching of near-IR face images to visible light face images an interesting and useful challenge. Image pre-processing techniques can potentially be used to help reduce the differences between near-IR and visible light images, with the goal of improving matching accuracy. We evaluate the use of several such techniques in combination with commercial matchers and show that simply extracting the red plane results in a comparable improvement in accuracy. In addition, we show that many of the pre-processing techniques hinder the ability of existing commercial matchers to extract templates. We also make available a new dataset called Near Infrared Visible Light Database (ND-NIVL) consisting of visible light and near-IR face images with accompanying baseline performance for several commercial matchers.