![]() proposed a facial feature extraction technique to detect cyber abuse activities. In several approaches, the identification framework focuses on relevant extracted information in real cases. ![]() The classification of facial emotions allows us to extract important information of the human face and describe well his cognitive state. The proposed framework can be easily applied for any security domain that needs to effectively distinguish the fear cases recognition.Īutomated emotion recognition is an interesting task that controls the detection method. The confidence interval for both of PPV and NPV is 92–98%. 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. Compared to other state-of-art and classification strategies, we recorded the highest accuracy of identified fear emotion. The obtained results have reached an encouraging accuracy up to 20° of AD. 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. In fact, a 3D/2D projection method is highlighted in order to deal with angular variation (AD) and orientation effects on the emotion detection. 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. 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.
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