LSTM model for visual speech recognition through facial expressions

Bhaskar, Shabina; Thasleema, T. M.

doi:10.1007/s11042-022-12796-1

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…Some factors such as lower image resolution, occlusion, and pose changes will make the expression discrimination and understanding more challenging [45]. Facial expression recognition that incorporates multimodal information is helpful to realizing more detailed emotional understanding, and it is the future development direction [46,47]. A question worthy of in-depth study is whether the two algorithms can always maintain good computing power and generalization performance.…”

Section: Discussionmentioning

confidence: 99%

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Bie

Gao

et al. 2022

Wireless Communications and Mobile Computing

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With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.

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Section: Discussionmentioning

confidence: 99%

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Bie

Gao

et al. 2022

Wireless Communications and Mobile Computing

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“…In sequence prediction problems, classifiers take an input instance and assign it to one of the available classes. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that has found extensive use in applications such as speech recognition [ 31 ], text classification [ 32 ], and ECG biometrics [ 33 ]. In this field, a hybrid model called CNN-LSTM is utilized.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

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“…For forecasting, it might learn the nonlinear relationship. Image processing [14,15], soft sensor modeling [16], energy consumption [17], speech recognition [18,19], sentiment analysis [20], and autonomous systems [21] are just few of the fields where LSTM has been applied. According to the survey [22] on the employment of deep learning algorithms to tackle the velocity prediction problem, LSTM is still the most preferred.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

et al. 2022

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Traffic speed forecasting in the short term is one of the most critical parts of any intelligent transportation system (ITS). Accurate speed forecasting can support travelers’ route choices, traffic guidance, and traffic control. This study proposes a deep learning approach using long short-term memory (LSTM) network with tuning hyper-parameters to forecast short-term traffic speed on an arterial parallel multi-lane road in a developing country such as Vietnam. The challenge of mishandling the location data of vehicles on small and adjacent multi-lane roads will be addressed in this study. To test the accuracy of the proposed forecasting model, its application is illustrated using historical voyage GPS-monitored data on the Le Hong Phong urban arterial road in Haiphong city of Vietnam. The results indicate that in comparison with other models (e.g., traditional models and convolutional neural network), the best performance in terms of root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE) is obtained by using the proposed model.

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LSTM model for visual speech recognition through facial expressions

Cited by 20 publications

References 39 publications

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

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