Real Time 3D Facial Emotion Classification using a Digital Signal PIC Microcontroller

Fnaiech, Ahmed; Bouzaiane, Sami; Sayadi, Mounir; Louis, Nicolas; Gorce, Philippe

doi:10.1109/ipas.2018.8708856

Cited by 2 publications

(3 citation statements)

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“…Experimental results demonstrate that the proposed methodologies are strong and effective for the fear facial recognition. Compared to negative facial emotion recognition methods using neural network [20], the proposed method has the highest values of classification percentage, which can achieve almost 97%, even in images containing AD and multiple orientations. We conclude that our method is more consistent than numerous supplementary approaches and can supply a high categorization rate.…”

Section: Discussionmentioning

confidence: 98%

“…This is due to the included confusion with other negative emotions. In this paper, only two classes (fear, others) were considered, compared to other state-of-art that are focused on seven emotion groups [20]. The latest research studies proved an important overlapping in view of fear emotion.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, an extended version of [20] is introduced in which many details are considered to get truthful performance assessment of the proposed scheme: The used classification hyperparameters are exhibited to demonstrate their influence on the categorization results. An improved training network construction is based on the combination of the principal component analysis (PCA) algorithm and the artificial neural network (ANN) classifier (PCAN) using the cross-validation method.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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