We present a novel approach for face recognition based on the physiological information extracted from thermal facial images. First, we delineate the human face from the background using a Bayesian method. Then, we extract the blood vessels present on the segmented facial tissue using image morphology. The extracted vascular network produces contour shapes that are unique for each individual. The branching points of the skeletonized vascular network are referred to as thermal minutia points (TMPs). These are reminiscent of the minutia points produced in fingerprint recognition techniques. During the classification stage, local and global structures of TMPs extracted from test images are matched with those of database images. We have conducted experiments on a large database of thermal facial images collected in our lab. The good experimental results show that our proposed approach has merit and promise.