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

• Main Authors: Lalitha, S. D, Thyagharajan, K. K
• Format: Journal Article
• Language: English
• Published: 29-09-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Computer Science - Multimedia
• DOI: 10.48550/arxiv.2009.13792

Facial expression is a standout amongst the most imperative features of human
emotion recognition. For demonstrating the emotional states facial expressions
are utilized by the people. In any case, recognition of facial expressions has
persisted a testing and intriguing issue with regards to PC vision. Recognizing
the Micro-Facial expression in video sequence is the main objective of the
proposed approach. For efficient recognition, the proposed method utilizes the
optimal convolution neural network. Here the proposed method considering the
input dataset is the CK+ dataset. At first, by means of Adaptive median
filtering preprocessing is performed in the input image. From the preprocessed
output, the extracted features are Geometric features, Histogram of Oriented
Gradients features and Local binary pattern features. The novelty of the
proposed method is, with the help of Modified Lion Optimization (MLO)
algorithm, the optimal features are selected from the extracted features. In a
shorter computational time, it has the benefits of rapidly focalizing and
effectively acknowledging with the aim of getting an overall arrangement or
idea. Finally, the recognition is done by Convolution Neural network (CNN).
Then the performance of the proposed MFEOCNN method is analysed in terms of
false measures and recognition accuracy. This kind of emotion recognition is
mainly used in medicine, marketing, E-learning, entertainment, law and
monitoring. From the simulation, we know that the proposed approach achieves
maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE)
value. These results are compared with the existing for MicroFacial Expression
Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion
Optimization (CNN+LO) and Convolutional Neural Network (CNN) without
optimization. The simulation of the proposed method is done in the working
platform of MATLAB.