Facial Expression Recognition Using Robust Local Directional Strength Pattern Features and Recurrent Neural Network This work proposes a novel facial expression recognition approach to contribute to better humanmachine interactions. To do that, edge features in facial expression images are combined with a recurrent neural network (RNN) to classify different facial expressions. Robust edge features are first obtained by using Local Directional Strength Pattern (LDSP) and applied with RNN. This LDSPRNN approach achieves superior recognition performance than other conventional approaches on a randomly distributed training and testing datasets obtained from a public dataset. The proposed approach should be useful for various practical applications such as a robot analyzing and understanding different human emotions from facial expressions based on robotic vision.

Deep Learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory 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 shortterm 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 inputdata from the features. The accuracy and computing time of this technique were studied. With LSTMRNNs, the results show that the design gives enhanced outcomes compared with other methods, including most imagevideo face detection methods. The efficiency evaluation of LSTMRNNs in images and in video frame series shows that there are performance improvements of more than 5% compared with traditional neural networks.