Deep hybrid learning for facial expression binary classifications and predictions

Abstract

• The Deep Hybrid Learning (DHL) Model is developed for facial expression classification. • Combined architecture with CNN, LSTM, and Transfer Learning are used. • The dropout layers are used to avoid higher computation time. • Different Epoch sizes are used to train the model. • Results are compared with existing models using accuracy, precision, recall, and f1-score. Image processing is a technique used for applying different operations to an image to produce an improved image or extract relevant information. Image processing has multiple applications in numerous fields, such as robotics, vision, pattern recognition, video processing, and the medical industry. One prominent application of facial recognition in image processing is identifying human expression. This research examines the accuracy of categorizing human facial expressions as happy or angry with deep learning and transfer learning methods such as CNN, LSTM, Inception, ResNet, VGG, Xception, and InceptionResnet. The proposed deep hybrid learning (DHL) approach classifies facial expressions using transfer learning and deep neural networks. This approach emphasizes the enhancement of prediction and classification by combining multiple deep learning models to perform better than a single model. The proposed model has a testing accuracy of 81.42% and a training accuracy of 95.93% with a multisource image dataset.