Convolutional Neural Network Driven Computer Vision Based Facial Emotion Detection and Recognition

Abstract

Computer vision is a sub branch of artificial intelligence (AI) that enables computers and systems to derive substitutive information from digital images and Video. Artificial intelligence plays a significant role in the area of security and surveillance, image processing and machine learning. In computer vision and image processing object detection algorisms are used to detect objects from certain classes of images or video. There is a scope identification of human face emotion Facial emotion recognition is done using computer vision algorism whether the person’s emotion is Happy, sad, fear, disgust, neutral and so on. Object detection algorism are used in deep learning used to classify the detected the regions. Facial emotion recognition is an emerging research area for improving human and computer interaction. It plays a crucial role in security, social communication commercial enterprise and law enforcement. In this research project CNN is used for training the data and predicting seven emotions such as anger, happy, sad, disgust, fear neutral and surprise. In this paper the experiment will be conduct using convolutional neural network as classifier, since it is multi class classification relu, softmax (activation function), categorical cross entropy(loss function) dropout max pooling conducted. The researcher tried to train the model by 80/20, 70/30, 90/10 train test split. However 70/30 train test split out performs over the other. The performance of the model is measured by using the epoch 10 and dropout 0.3. Totally the model is performed 93.8% in the training accuracy and it 75% for the testing.