ET-CycleGAN: Generating Thermal Images from Images in the Visible Spectrum for Facial Emotion Recognition

Pons, Gérard; Ali, Abdallah El; César, Pablo

doi:10.1145/3395035.3425258

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Pons et al [74] developed an Emotion-Guided Thermal CycleGAN (ET-CycleGAN) for classifying sixth facial emotions. To prevent overfitting, batch normalization was used.…”

Section: ) Temperature Variation Extraction a Deep Learning Methodsmentioning

confidence: 99%

A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

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Estimated vital signs might include a variety of measurements that can be used in detecting any abnormal conditions by analyzing facial images from continuous monitoring with a thermal video camera. To overcome the limitless human visual perceptions, thermal infrared has proven to be the most effective technique for visualizing facial colour changes that could have been reflected by changes in oxygenation levels and blood volume in facial arteries. This study investigated the possibility of vital signs estimation using physiological function images converted from the thermal infrared images in the same ways that visible images are used, with a need for an efficient extractor method as correction procedures that have used datasets that include images with and without wearing glasses or protective face masks. This paper, summarize thermal images using advanced machine learning and deep learning methods with satisfactory performance. Also, we presented the evaluation matrices that were included in the assessment based on statistical analysis, accuracy measures and error measures. Finally, to discuss future gaps and directions for further evaluations.INDEX TERMS Thermal images, features extractions, vital signs estimation, evaluation matrices.

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“…Pons et al [74] developed an Emotion-Guided Thermal CycleGAN (ET-CycleGAN) for classifying sixth facial emotions. To prevent overfitting, batch normalization was used.…”

Section: ) Temperature Variation Extraction a Deep Learning Methodsmentioning

confidence: 99%

A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

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“…Today, WCE devices are produced by different manufacturers and offer a range of technical specifications [20,21]. Pons et al determined the performance before and after domain adaptation using a receiver operating characteristic curve (Figure 3) [19]. The performance of the baseline image before domain adaptation was extremely poor (area under the curve (AUC), 0.6488).…”

Section: Using Triplet Loss For Domain Adaptation In Wireless Capsule...mentioning

confidence: 99%

The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging

Kim,

Kim

et al. 2023

Diagnostics

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Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.

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Mobilenetv3: a deep learning technique for human face expressions identification

Prasad¹,

Chandana²

2023

Int. j. inf. tecnol.

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ET-CycleGAN: Generating Thermal Images from Images in the Visible Spectrum for Facial Emotion Recognition

Cited by 4 publications

References 21 publications

A Review of Facial Thermography Assessment for Vital Signs Estimation

A Review of Facial Thermography Assessment for Vital Signs Estimation

The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging

Mobilenetv3: a deep learning technique for human face expressions identification

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