WTAOF-ILPB Based Feature Learning and LFSSOA-RBFNN Based Classification for Facial Micro-Expression Recognition

Micro-Expressions (ME) has turned out to be one amongst the most vital clues for lies, as well as numerous other applications. It is the brief Facial Expressions (FE) that the people express whilst endeavouring to conceal, hide, or repress their emotions. In a real environment, recognizing facial ME is really very hard than recognizing the general-FE, which shows rich emotions. Here, to attain the most accurate classification outcomes of Micro-Expression Recognition (MER), a combination of Feature Learning (FL) algorithms is proposed. The Video Frames (VF) is taken as the input for MER. First, the inputted-VF is preprocessed and then a Viola-Jones Algorithm is carried out to attain the face images of the VF. The SB-DCNN extracts the Facial Land-Marks as of the face images. Next, for performing the effectual FL, the combination of Weight matrix-based Temporal Accumulated Optical Flow and Improved LBP features (WTAOF-ILPB) are employed. Lastly, to recognize the ME of the input VF, the features as of SB-DCNN and WTAOF-ILPB are rendered to the optimal adaptation of Radial Basis Function Neural Network (RBFNN), i.e. Levy Flight based Shuffled Shepherd Optimization Algorithm (LFSSOA)–RBFNN (LFSSOA-RBFNN). The proposed method’s performance is analyzed by executing an experiment. The outcomes exhibited that the proposed LFSSOA-RBFNN trounces other prevailing algorithms with respect to some performance metrics.