Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition

Tannugi, Dylan C.; Britto, Alceu S.; Koerich, Alessandro L.

doi:10.1109/smc.2019.8914172

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Several studies have achieved high accuracy rates for facial expression recognition using CNNs. For instance, Shaees et al achieved 99.3% accuracy on thermal images using the Natural Visible and Infrared Expression (NVE) database and 98.3% accuracy on graphic illustrations using the Cohn-Kanade+ (CK+) database [28]. Similarly, Webb et al achieved a state-of-the-art classification rate of 99.52% on a combined corpus of datasets [29].…”

Section: Deep Learning Algorithmmentioning

confidence: 99%

Effectiveness of Using Artificial Intelligence for Early Child Development Screening

Gau¹,

Ting²,

Toh

et al. 2023

Green Intell. Syst. Appl.

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This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.

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Section: Deep Learning Algorithmmentioning

confidence: 99%

Effectiveness of Using Artificial Intelligence for Early Child Development Screening

Gau¹,

Ting²,

Toh

et al. 2023

Green Intell. Syst. Appl.

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“…Particularly, FER2013 [15], TFD [16] or, more recently, RAF-DB [65,66] datasets are good sources of additional data for FER tasks. Tannugi et al [67] and Li and Deng [68] pursued interesting work on cross-dataset generalization tasks by switching in turn source and target FER datasets and evaluating the performance of FER models. Li and Deng [68] showed that datasets are strongly biased, and they developed a novel architecture that can learn domain-invariant and discriminative features.…”

Section: Pre-training and Fine-tuning Of 2d Cnnsmentioning

confidence: 99%

Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

2021

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Facial expressions are one of the most powerful ways to depict specific patterns in human behavior and describe the human emotional state. However, despite the impressive advances of affective computing over the last decade, automatic video-based systems for facial expression recognition still cannot correctly handle variations in facial expression among individuals as well as cross-cultural and demographic aspects. Nevertheless, recognizing facial expressions is a difficult task, even for humans. This paper investigates the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) to deal with long video sequences captured in the wild for continuous emotion recognition. For such an aim, several 2D CNN models that were designed to model spatial information are extended to allow spatiotemporal representation learning from videos, considering a complex and multi-dimensional emotion space, where continuous values of valence and arousal must be predicted. We have developed and evaluated convolutional recurrent neural networks, combining 2D CNNs and long short term-memory units and inflated 3D CNN models, which are built by inflating the weights of a pre-trained 2D CNN model during fine-tuning, using application-specific videos. Experimental results on the challenging SEWA-DB dataset have shown that these architectures can effectively be fine-tuned to encode spatiotemporal information from successive raw pixel images and achieve state-of-the-art results on such a dataset.

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“…We can particularly mention FER2013 (Goodfellow et al, 2013), TFD (Susskind et al, 2010) or more recently RAF-DB (Li et al, 2017a;Li & Deng, 2019) datasets as good sources of additional data for FER tasks. Besides, Tannugi et al (2019) and Li & Deng (2020) pursued interesting work on cross-dataset generalization task by switching in turn source and target FER datasets and evaluating performance of FER models. Li & Deng (2020) have shown that datasets are strongly biased and they have developed accordingly, novel architecture that can learn domain-invariant and discriminative features.…”

Section: Pre-training and Fine-tuning Of 2d-cnnsmentioning

confidence: 99%

Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

Teixeira¹,

Granger²,

Koerich³

2020

Preprint

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The attention in affect computing and emotion recognition has increased in the last decade. Facial expressions are one of the most powerful ways for depicting specific patterns in human behavior and describing human emotional state. Nevertheless, even for humans, identifying facial expressions is difficult, and automatic video-based systems for facial expression recognition (FER) have often suffered from variations in expressions among individuals, and from a lack of diverse and cross-culture training datasets. However, with video sequences captured in-the-wild and more complex emotion representation such as dimensional models, deep FER systems have the ability to learn more discriminative feature representations. In this paper, we present a survey of the state-of-theart approaches based on convolutional neural networks (CNNs) for long video sequences recorded with in-the-wild settings, by considering the continuous emotion space of valence and arousal. Since few studies have used 3D-CNN for FER systems and dimensional representation of emotions, we propose an inflated 3D-CNN architecture, allowing for weight inflation of pre-trained 2D-CNN model, in order to operate the essential transfer learning for our video-based application. As a baseline, we also considered a 2D-CNN architecture cascaded network with a long short term memory network, therefore we could finally conclude with a model comparison over two approaches for spatiotemporal representation of facial features and performing the regression of valence/arousal values for emotion prediction. The experimental results on RAF-DB and SEWA-DB datasets have shown that these fine-tuned architectures allow to effectively encode the spatiotemporal information from raw pixel images, and achieved far better results than the current state-of-the-art.

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Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition

Cited by 6 publications

References 25 publications

Effectiveness of Using Artificial Intelligence for Early Child Development Screening

Effectiveness of Using Artificial Intelligence for Early Child Development Screening

Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

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