Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum

We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMP) and constitute the feature database. To render the method robust to facial pose variations we collect for each subject to be stored in the database five (5) different pose images (center, mid-left profile, left profile, mid-right profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a sizeable database of thermal facial images collected in our lab. The good experimental results show that the proposed methodology has merit. More important, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.