Common Feature Discriminant Analysis for Matching Infrared Face Images to Optical Face Images

In biometrics research and industry, it is critical yet a challenge to match infrared face images to optical face images. The major difficulty lies in the fact that a great discrepancy exists between the infrared face image and corresponding optical face image because they are captured by different devices (optical imaging device and infrared imaging device). This paper presents a new approach called common feature discriminant analysis to reduce this great discrepancy and improve optical-infrared face recognition performance. In this approach, a new learning-based face descriptor is first proposed to extract the common features from heterogeneous face images (infrared face images and optical face images), and an effective matching method is then applied to the resulting features to obtain the final decision. Extensive experiments are conducted on two large and challenging optical-infrared face data sets to show the superiority of our approach over the state-of-the-art.

I. Introduction

Traditional optical imaging devices require appropriate illumination conditions to work properly, which is difficult to achieve satisfactorily in practical face recognition applications. To combat low illumination at nights or indoors, infrared imaging devices have been widely applied to many automatic face recognition (ARF) systems. The task of infrared-based ARF systems is to match a probe face image taken with the infrared imaging device to a gallery of face images taken with the optical imaging device, which is considered to be an important application of heterogeneous face recognition [1] (also known as cross-modality face recognition). The most challenging issue in heterogeneous face recognition is that face images associated with the same person but taken with different devices may be mismatched due to the great discrepancy between the different image modalities (optical and infrared), which is referred to as modality gap. As illustrated in Fig. 1, the infrared photos are usually blurred, low contrast, and have significantly different gray distribution compared to the optical photos. Fig. 1.

Examples of optical and corresponding infrared face images for (a) HFB dataset and (b) CUHK VIS-NIR dataset.