Emotion Recognition in Human Face Through Video Surveillance—A Survey of State-of-the-Art Approaches

Kant, Krishna; Shah, D. B.

doi:10.1007/978-981-19-0095-2_6

Cited by 1 publication

(1 citation statement)

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“…On the other hand, recent research has explored the potential of implementing emotion recognition technology based on deep learning (DL) [27][28][29] in video surveillance systems for large groups of people. These studies have shown promising results in detecting emotions such as Anger, Fear, Joy, and Sadness from facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

Kalyta,

Barmak,

Radiuk

et al. 2023

Applied Sciences

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Modern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person’s emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric interpretation of facial areas to extract features of facial expression, the method of hyperplane classification for identifying emotional states in the feature vector space, and the principles of visual analytics and “human in the loop” to obtain transparent and interpretable classifiers. The experimental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91–2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23–2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.

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Section: Introductionmentioning

confidence: 99%