During the initial briefing presentation, there were a lot of points to gather our attention for further investigation and possible methods and tools are one of them. In this blog, this has been my prior concern to focus on.
Group-based emotion recognition (GER) is an interesting topic in both security and social area. In our work, by utilizing the Neural Network, emotion recognition (ER) can be performed from a group of people. Initially, original video frames are taken as input and pre-process from multi-user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features will be selected using the Modified Sea-lion optimization algorithm process. Finally, a recurrent fuzzy neural network (RFNN) classifier-based Social Ski-Driver (SSD) optimization algorithm is proposed for the classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall, and precision is evaluated with some existing techniques.
The above-named methods are one of the many possible solutions to the problem domain. They just become more suitable due to the high accuracy in the final result. Still, there is a need for further investigations on the yet unrevealed ones and to compare the possible cons and pros.
You should try to register for this challenge and see if you can get access to their data, even though the challenge is done: https://sites.google.com/view/emotiw2020