Convolutional Neural Network for the Recognition and Characterization of Emotions using Double Average Filtering and SELU activation – Valence Cognizance (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
An Emotion being a complex psychological state that involves both experience and action becomes achallenge to be recognized accurately by programmable codes. This paper demonstrates the method of identifyingeach out of seven basic emotional states (happiness, surprise, fear, anger, fear, disgust, sadness and neutral) andcharacterizing them as either positive or negative (valence) from images in a given dataset. This has beenachieved to a higher accuracy by a Convolutional Neural Network designed with Double Average filters and theSELU (Scaled Exponential Linear Unit) activation units. The images from the FER 2013 dataset is processed(converted to gray scale and the dimensions set to 48x48) and given as input to the CNN. The Double AverageFilters remove the noises much more efficiently than Average Filters, since the process is repeated to give evenlesser intensity variations between the pixels. The SELU activation used in the CNN gives an internalnormalization on the filtered images, which results in a much better identifying of emotions than with otheractivation unit. The SELU in recent times, as mentioned by other researchers too, is a promising part of anynetworks that can be used in Machine Learning. The proposed novel CNN model has a training accuracy of morethan 96.53%.