Facial Expression Recognition with CNN-LSTM

Hùng, Bùi Thanh; Tien, Le Minh

doi:10.1007/978-981-15-7527-3_52

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…Moreover, the context-sensitivity of expressions can be a limitation [43], as static images often fail to capture the full emotional context. The limited and biased nature of training data can hinder the model's ability to generalise, while real-time processing in dynamic environments remains a computationally intensive task.…”

Section: Facial Expression Analysismentioning

confidence: 99%

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A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

Shree,

Mohapatra,

Suwanwiwat

et al. 2024

Preprint

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In suspect identification systems, facial features play a crucial role in recognising individuals. However, the challenge lies in sustaining the accuracy of the system over a long period of time, ensuring that it remains consistently high, reliable, and effective. This research introduces a novel lightweight model that requires low trainable parameters, a significantly smaller number than pre-trained models, which use millions of trainable parameters. The newly proposed recurrent feature iterative network integrates a convolutional neural network and long short-term memory in a single structure to synthesise diverse images and to effectively extract facial features. The long short-term memory-based feature-recurrent system demonstrates a significant improvement in accuracy when tested on the augmented reality face database, enhanced extended Yale B, Cohn-Kanade and extended Yale B datasets, achieving encouraging accuracy rates of 99.23%, 99.16%, 98.99%, and 97.68%, respectively. These accuracies, in general, outperform traditional baselines of 68.65%. This research advances the field by providing an innovative solution to enhance suspect identification systems through advanced facial image feature extraction, resulting in significantly improved accuracy rates.

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Section: Facial Expression Analysismentioning

confidence: 99%

“…This provides a test accuracy of 78.52% and a validation accuracy of 78.9%. A research [43] introduced a set of concepts for recognising facial expressions using CNN+LSTM. CNN is used to extract features [4], and LSTM is used to identify them.…”

Section: Facial Expression Analysismentioning

confidence: 99%

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

Shree,

Mohapatra,

Suwanwiwat

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Authors reported that their approach outperformed state-of-the-art approaches, including BERT ( Devlin et al, 2019 ), XLNet ( Yang et al, 2019 ), and RoBERTa ( Liu et al, 2019 ), in terms of accuracy. Numerous studies have used the CNN-LSTM technique to solve various problems in a variety of domains, including wind speed forecast ( Chen et al, 2021 ), facial expression recognition ( Hung & Tien, 2021 ), emotion detection ( Abdullah, Hadzikadicy & Shaikhz, 2019 ), hate speech detection ( Duwairi, Hayajneh & Quwaider, 2021 ), and spam detection ( Ghourabi, Mahmood & Alzubi, 2020 ).…”

Section: Related Literaturementioning

confidence: 99%

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Shambour¹

2022

PeerJ Computer Science

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Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020–2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1–13, 1442 (July 11–23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people’s impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

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“…Facial recognition ranges from identifying one's identity to deciphering their emotions. Expression recognition often relies on a CNN for extraction of important features from image data before that image data can be used by the RNN [6]. Once these features are deciphered the LSTM RNN can make a prediction about the emotion perceived.…”

Section: Introductionmentioning

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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Facial Expression Recognition with CNN-LSTM

Cited by 15 publications

References 16 publications

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

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