applying partial differential equations on cubic uniform local binary pattern to reveal micro-changes

Background and objectives: the world we live in everyday, accompany with enormous numbers of minute variations which affect us and our surroundings in several aspects. these variations, so called micro-changes, are low in intensity and brief in duration which makes them almost invisible to naked eyes. nonetheless, revealing them could open up a new wide range of applications from security, business, engineering, medical, and seismology to psychology. methods: in this paper, we adopted a novel autonomous approach comprising partial differential equations (pde) and cubic uniform local binary pattern (cubic-ulbp) to spot micro-changes. cubic-ulbp extracts 15 planes containing robust features against noise and illumination variations. afterwards, pde pick out single plane out of 15 to reduce time consumption. results: the proposed method is not only optimized to get the job done perfectly but also provides promising results comparing with most state-of-the-art methods. so that the accuracy is increased about 36% and 40% on the casme and the casme ii databases, respectively. conclusion: the combination of the pde and the cubic-ulbp creates a strong and optimal method for detecting the apex frame and micro-movement. this method's performance is found to be promising when the training samples are scarce, too.