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Facial expression is a standout amongst the most imperative features of humanemotion recognition. For demonstrating the emotional states facial expressionsare utilized by the people. In any case, recognition of facial expressions haspersisted a testing and intriguing issue with regards to PC vision. Recognizingthe Micro-Facial expression in video sequence is the main objective of theproposed approach. For efficient recognition, the proposed method utilizes theoptimal convolution neural network. Here the proposed method considering theinput dataset is the CK+ dataset. At first, by means of Adaptive medianfiltering preprocessing is performed in the input image. From the preprocessedoutput, the extracted features are Geometric features, Histogram of OrientedGradients features and Local binary pattern features. The novelty of theproposed method is, with the help of Modified Lion Optimization (MLO)algorithm, the optimal features are selected from the extracted features. In ashorter computational time, it has the benefits of rapidly focalizing andeffectively acknowledging with the aim of getting an overall arrangement oridea. Finally, the recognition is done by Convolution Neural network (CNN).Then the performance of the proposed MFEOCNN method is analysed in terms offalse measures and recognition accuracy. This kind of emotion recognition ismainly used in medicine, marketing, E-learning, entertainment, law andmonitoring. From the simulation, we know that the proposed approach achievesmaximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE)value. These results are compared with the existing for MicroFacial ExpressionBased Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with LionOptimization (CNN+LO) and Convolutional Neural Network (CNN) withoutoptimization. The simulation of the proposed method is done in the workingplatform of MATLAB.

Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm