8D-THERMO CAM: Combination of Geometry with Physiological Information for Face Recognition

Biometrics-based technologies in the area of identity management are gaining increasing importance, as a means of establishing non-falsifiable credentials for end users. However, in the three-way tug-of-war between convenient, unobtrusive data collection (required for user acceptance), accuracy in results (required for justifying deployment), and speed (required for widespread use in practice), no single biometric to date has managed to hold the middle ground that would allow for its ready adoption. The overall goal of our project is to develop the theoretical framework and computational tools that will lead to the development of a practical, unobtrusive, and accurate face recognition system for convenient and effective access control. This framework encompasses 8D characteristics of the face (3D geometry+2D visible texture+2D infrared texture, over time). In this paper, we present a novel multi-modal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. From the fitted parametric model we extract two images corresponding to the subject's face and process these images to extract biometric signatures. Specifically, the deformation image is compressed using a wavelet transform and the vasculature graph is extracted from the parametric thermal image.