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 rec-ognition in image processing is identifying human expression. This research examines the accuracy of categoriz-ing 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) ap-proach 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.(c) 2022 Elsevier B.V. All rights reserved.
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