Face Recognition Using Infrared Images and Eigenfaces

Automated face recognition is a well studied problem in computer vision [4]. Its current applications include security (ATM’s, computer logins, secure building entrances), criminal photo (“mug-shot” databases, and human-computer interfaces. One of the more successful techniques of face recognition is principle component analysis, and specifically eigenfaces [1, 2, 3]. In this paper we describe the application of eigenface analysis to infrared facial images. Infrared images (or thermograms) represent the heat patterns emitted from an object. Since the vein and tissue structure of a face is unique (like a fingerprint), the infrared image should also be unique (given enough resolution, you can actually see the surface veins of the face). At the resolutions used in this study (160 by 120), we only see the averaged result of the vein patterns and tissue structure. However, even at this low resolution, infrared images give good results for face recognition. The only known usage of infrared images for face recognition is the by company Technology Recognition Systems [5]. Their system does not use principle component analysis, but rather simple histogram and template techniques. They do claim to have a very accurate system (which is even capable of telling identical twins apart), but they unfortunately have no published results which we could use for comparison.