Effect of different partitioning strategies of face imprint on thermal face recognition

Face recognition using thermal images has received high attention by researchers in recent years. Since skin temperature can be visualized using thermographic cameras, a unique imprint can be achieved for thermal image of a person's face. In this study, we have investigated the effect of using different partitioning strategies of face imprint on thermal face recognition. First, we have proposed a simple and reliable approach for face extraction using ellipse mask. Then, the face imprint has been attained using anisotropic diffusion filter and morphological processing. The face imprint is related to distribution of blood vessels under the face skin. In the next step, the face imprint has been partitioned using different strategies including rectangular, circular and polar approaches. The number of minutiae point in each partition has been computed. Finally, a SVM classifier based on one-against-one approach has been used for face recognition. This paper presents a framework to find the best partitioning approach resulting in highest performance. Based on the results, the rectangular partitioning was found as the best partitioning approach for face imprint, due to its highest performance (92.27%).