Emotion recognition at the edge with AI specific low power architectures

Nardo, Emanuel Di; Santopietro, Vincenzo; Petrosino, Alfredo

doi:10.1016/j.micpro.2021.104299

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…These results are fused with audio emotion classification results, which resulted in significant result values and a much higher accuracy level than expected in other audio-visual models. There is much recent research done on the latest CNN models such as InceptionNet VGG, Resnet, SqueezeNet, and many more with different combinations of novel approaches [19][20][21][22][23]. With time even though new deep learning methods, algorithms, and new FER datasets are being studied using novel approaches, the gist of FERs basic flow, illustrated in Figure 11, has always been the same for more than a decade.…”

Section: Qualitative Analysis Of Fer Research and Its Publicationmentioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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Facial expressions are mirrors of human thoughts and feelings. It provides a wealth of social cues to the viewer, including the focus of attention, intention, motivation, and emotion. It is regarded as a potent tool of silent communication. Analysis of these expressions gives a significantly more profound insight into human behavior. AI-based Facial Expression Recognition (FER) has become one of the crucial research topics in recent years, with applications in dynamic analysis, pattern recognition, interpersonal interaction, mental health monitoring, and many more. However, with the global push towards online platforms due to the Covid-19 pandemic, there has been a pressing need to innovate and offer a new FER analysis framework with the increasing visual data generated by videos and photographs.Furthermore, the emotion-wise facial expressions of kids, adults, and senior citizens vary, which must also be considered in the FER research. Lots of research work has been done in this area. However, it lacks a comprehensive overview of the literature that showcases the past work done and provides the aligned future directions. In this paper, the authors have provided a comprehensive evaluation of AI-based FER methodologies, including datasets, feature extraction techniques, algorithms, and the recent breakthroughs with their applications in facial expression identification. To the best of the author's knowledge, this is the only review paper stating all aspects of FER for various age brackets and would significantly impact the research community in the coming years.

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Section: Qualitative Analysis Of Fer Research and Its Publicationmentioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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“…Статьи [17,18] демонстрируют возможность размещения и осуществления работы на мобильных устройствах и встраиваемых системах, построенных на базе глубокого машинного обучения.…”

Section: Introductionunclassified

Method for Detecting Suspicious Transactions of Banking Clients Based on Emotion Recognition System

2024

Cybersecurity Issues

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Цель статьи: разработка метода выявления транзакций, совершенных клиентами, подвергнутыми воздействию мошенников с помощью методов социальной инженерии, на основе анализа видеоданных лица с использованием нейросетевых методов распознавания эмоций. Метод исследования: анализ современных нейросетевых моделей и подходов, используемых для решения задачи распознавания эмоций; анализ архитектур нейронных сетей, обрабатывающих видеоизображение или последовательность кадров; разработка и программная реализация метода обнаружения подозрительных транзакций с использованием искусственных нейронных сетей по видеоданным лица человека; экспериментальное исследование и оценка разработанного метода. Полученный результат: разработан метод выявления подозрительных транзакций, основанный на нейросетевых методах распознавания лицевых эмоций клиентов банка, подвергшихся воздействию злоумышленников. Реализована комбинированная структура нейронной сети с использованием архитектуры пригодной для обработки графической информации и информации, представленной во временной последовательности, для решения задачи распознавания эмоций. Создан программный прототип, позволяющий оценивать эмоциональное состояние наблюдаемого человека по видеоданным лица и способный определять нахождение человека в негативном эмоциональном состоянии. Были проанализированы результаты разработанного метода. Даны рекомендации по перспективам его применения и дальнейшим исследованиям данной темы. Научная новизна: предложен новый метод выявления подозрительных транзакций, основанный на решении задачи распознавания эмоций по видео с применением комбинации CNN и LSTM архитектур нейронных сетей.

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A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification

Siddique

Iqbal

Aleem

et al. 2023

Microprocessors and Microsystems

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Emotion recognition at the edge with AI specific low power architectures

Cited by 4 publications

References 5 publications

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

Method for Detecting Suspicious Transactions of Banking Clients Based on Emotion Recognition System

A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification

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