On-the-Fly Facial Expression Prediction Using LSTM Encoded Appearance-Suppressed Dynamics

Baddar, Wissam J.; Lee, Sangmin; Ro, Yong Man

doi:10.1109/taffc.2019.2957465

Cited by 15 publications

(11 citation statements)

References 73 publications

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“…Several works use the characteristics to clarify the differences in shape and texture [14]. The extraction characteristics were then used to classify the face expression [38].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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“…Several works use the characteristics to clarify the differences in shape and texture [14]. The extraction characteristics were then used to classify the face expression [38].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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“…Second, a hybrid method that combines multiple networks was also used. A CNN-RNN or CNN-LSTM [39,40] structure is one of the examples. It learns spatial features with CNN and then learns temporal features by RNN or LSTM.…”

Section: Deep-learning-based Approachesmentioning

confidence: 99%

A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

Park

Kim

Chilamkurti

2021

Sensors

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In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.

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“…With their proposed method [29], they were able to attain 99% on CK + dataset, 81.60% on MMI, 56.68% on SFEW (which is highly accurate for that dataset), and 95.21% on their own dataset. Other similar methodologies [30,31] were also able to benefit from the LSTM gate implemented in their models and were evaluated against the MMI dataset. The model from [30] was able to achieve an impressive accuracy of 92.07%, and the proposed method from [31] attained an accuracy of 82.97%.…”

Section: Model For Facial Expression Recognition Using Lstm Rnnmentioning

confidence: 99%

“…Other similar methodologies [30,31] were also able to benefit from the LSTM gate implemented in their models and were evaluated against the MMI dataset. The model from [30] was able to achieve an impressive accuracy of 92.07%, and the proposed method from [31] attained an accuracy of 82.97%.…”

Section: Model For Facial Expression Recognition Using Lstm Rnnmentioning

confidence: 99%

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

2021

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Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each proposed model.

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On-the-Fly Facial Expression Prediction Using LSTM Encoded Appearance-Suppressed Dynamics

Cited by 15 publications

References 73 publications

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

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