Convolution Neural Network Based Emotion Classification Cognitive ModelforFacial Expression
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Abstract
Facial expression is a structured communicative approach in building relationships and
interacting with others. It can be easy to focus on sensitivity and emotional content of mental
state, personality, behavioral and intention of persons.The human behavior model makes
enlighten on automatic facial expression recognition system.In Human-Machine Interaction
(HMI), recognition of facial expressions is automated and it is considered as important
component of natural communication. The paper proposes Convolutional Neural
Networks(CNN) based emotion classification cognitive model for facial expression.The model
classifiespositive and negative images which significantly specify regions within an image and
network performance is depend on different training options. A rectangular box is drawn around
the facial image and output is formatted above the rectangular box. Kaggle facial expression
FER-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 condition
testing data set is comparing with proposed model, the highest accuracy for happy emotion with
99%, followed by surprise with 98%, neutral with 96% and least accuracy for fear emotion with
45%. Live validity test is obtained with a webcam resolution of 320x240 and the network input
layer is 224x224 with 50 cm distance is maintained between the webcam and face.
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