A generalized mechanism beyond NLP for real-time detection of cyber abuse through facial expression analytics

Shome, Atanu; Rahman, Md. Mizanur; Chellappan, Sriram; Islam, A. B. M. Alim Al

doi:10.1145/3360774.3360793

Cited by 4 publications

(3 citation statements)

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“…For example, people generally infer emotional states such as happiness, sadness, fear, and anger from facial expressions and tone of voice. Therefore, facial emotion recognition (FER) [1][2][3][4] has been studied in computer vision and machine learning over the past several decades. Indeed, FER technology is rapidly emerging in the field of emotional Information and Communication Technology (ICT), including virtual reality, augmented reality, Advanced Driver Assistance Systems (ADAS), and human-computer interaction.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Negative Emotion Recognition in Visible Images Enhanced by Thermal Imaging

Lee

Bak

et al. 2022

Sustainability

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Facial expressions help in understanding the intentions of others as they are an essential means of communication, revealing human emotions. Recently, thermal imaging has been playing a complementary role in emotion recognition and is considered an alternative to overcome the drawbacks of visible imaging. Notably, a relatively severe recognition error of fear among negative emotions frequently occurs in visible imaging. This study aims to improve the recognition performance of fear by using the visible and thermal images acquired simultaneously. When fear was not recognized in a visible image, we analyzed the causes of misrecognition. We thus found the condition of replacing the image with a thermal image. It improved emotion recognition performance by 4.54% on average, compared to the performance of using only visible images. Finally, we confirmed that the thermal image effectively compensated for the visible image’s shortcomings.

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Section: Introductionmentioning

confidence: 99%

Improvement of Negative Emotion Recognition in Visible Images Enhanced by Thermal Imaging

Lee

Bak

et al. 2022

Sustainability

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“…Facial emotion recognition (FER) [1][2][3][4] is an interesting field that is actually expanding considerably. This domain touches the psychological condition of a human being, his behavior, and his responses.…”

Section: Introductionmentioning

confidence: 99%

“…In several approaches, the identification framework focuses on relevant extracted information in real cases. Shome et al [1] 2 of 15 proposed a facial feature extraction technique to detect cyber abuse activities. They accomplished an integrated real-time detection module.…”

Section: Introductionmentioning

confidence: 99%

Fear Facial Emotion Recognition Based on Angular Deviation

et al. 2021

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This paper shows an advanced method that is able to achieve accurate recognition of fear facial emotions by providing quantitative evaluation of other negative emotions. The proposed approach is focused on both a calibration computing procedure and an important feature pattern technique, which is applied to extract the most relevant characteristics on different human faces. In fact, a 3D/2D projection method is highlighted in order to deal with angular variation (AD) and orientation effects on the emotion detection. Using the combination of the principal component analysis algorithm and the artificial neural network method (PCAN), a supervised classification system is finally achieved to recognize the considered emotion data split into two categories: fear and others. The obtained results have reached an encouraging accuracy up to 20° of AD. Compared to other state-of-art and classification strategies, we recorded the highest accuracy of identified fear emotion. A statistical analysis is carried out on the whole facial emotions, which confirms the best classification performance (positive predictive values (PPV) = 95.13, negative predictive values (NPV) = 94.65, positive likelihood ratio (PLr) = 33.9, and negative likelihood ratio (NLr) = 0.054. The confidence interval for both of PPV and NPV is 92–98%. The proposed framework can be easily applied for any security domain that needs to effectively distinguish the fear cases recognition.

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