Convolution Neural Network Based Emotion Classification Cognitive ModelforFacial Expression (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Facial expression is a structured communicative approach in building relationships andinteracting with others. It can be easy to focus on sensitivity and emotional content of mentalstate, personality, behavioral and intention of persons.The human behavior model makesenlighten on automatic facial expression recognition system.In Human-Machine Interaction(HMI), recognition of facial expressions is automated and it is considered as importantcomponent of natural communication. The paper proposes Convolutional NeuralNetworks(CNN) based emotion classification cognitive model for facial expression.The modelclassifiespositive and negative images which significantly specify regions within an image andnetwork performance is depend on different training options. A rectangular box is drawn aroundthe facial image and output is formatted above the rectangular box. Kaggle facial expressionFER-2013 Databasewith seven facial expression labels as happy, neutral, surprise, fear, anger,disgust, and sad is implemented. The evaluation of model shows that accuracy of lab conditiontesting data set is comparing with proposed model, the highest accuracy for happy emotion with99%, followed by surprise with 98%, neutral with 96% and least accuracy for fear emotion with45%. Live validity test is obtained with a webcam resolution of 320x240 and the network inputlayer is 224x224 with 50 cm distance is maintained between the webcam and face.