Abstract
In recent years, deep learning neural frameworks have been given significant attention in programming development, especially in machine learning, machine vision and artificial intelligence (AI). The ability to detect faces has inspired many researchers because a human face shows great dissimilarities in form and figure due to changes in position and expression in different situations. This research aims to produce a method of applying recurrent neural network (RNN) designs using long short-term memory (LSTM) to identify facial expressions. The proposed method involves an improved RNN that uses LSTM to increase the effectiveness of the feature extraction process using input sets which regenerate the input-data from the features. The accuracy and computing time of this technique were studied. With LSTM-RNNs, the results show that the design gives enhanced outcomes compared with other methods, including most image/video face detection methods. The efficiency evaluation of LSTM-RNNs in images and in video frame series shows that there are performance improvements of more than 5% compared with traditional neural networks.
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Salih, W.M., Nadher, I., Tariq, A. (2020). Deep Learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_19
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DOI: https://doi.org/10.1007/978-3-030-38752-5_19
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