Robust Facial Expression Recognition Using a Smartphone Working against Illumination Variation

Active Appearance Model (AAM) (1, 4) has been favoured among computer vision researchers, since it is useful for tracking human face or diverse objects. Moreover, recent development of the inverse composition method allows us to relieve computational burden in tracking human face with the real-time basis. The progress of fast algorithm development makes it possible that we may be able to track a human face within video image feeding from a smartphone. However, such mobile device gives us some limitation in computing power, but also illumination environment around it is not favourable to vision algorithm designers. This paper presents new method by which such illumination variation occurred in the mobile environment can be overcome. We have found that the Difference of Gaussian (DoG) kernel preceded the AAM stage is very effective in tracking human face including important facial features such as eyes, nose and mouth, despite a strong directional illumination. Performance of the proposed system is evaluated for diverse illumination conditions, and result suggests that the DoG kernel, that has been identified as biologically plausible, can plays an important role against such odd illumination environment. This algorithm has been implemented on a smartphone for the purpose of human facial expression recognition and it works well with a camera as an application program.