Fear Facial Emotion Recognition Based on Angular Deviation

Abstract

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.

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

“…Although FER systems have recently been improved due to deep learning techniques and technological advances, there are still some limitations to overcome, which include the following: Lack of diverse databases causing a need for the acquisition of new large databases with a high level of annotation quality [ 39 , 46 , 53 , 56 , 83 , 124 , 161 , 164 ]; The proposed methods do not provide better accuracy than the ones described in the literature, or the model achieved performance on par with state-of-the-art methods [ 49 , 50 , 92 ]; Misclassifications between emotions (such as ”sad” and “angry”) which indicates that the system needs further improvements [ 58 , 120 , 162 , 165 , 175 ]; Proposed architectures are usually characterized by high complexity [ 32 , 33 , 41 , 43 , 64 , 78 , 114 , 141 , 163 ]; Small number of recognized emotions [ 45 , 90 , 93 , 116 , 160 ]; The proposed model is built to recognize facial expressions on static images which may limit its applicability [ 68 , 73 ]. …”

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

“…Much research on general facial expression classification (or FER) has been conducted based on visible images [18][19][20][21]. Facial expression classification technology, based on visible light imaging that acquires an object's image by measuring the light reflected from the object, is sensitive to changes in lighting.…”

“…They then proposed a method of integrating the two models to improve the facial expression classification performance. Ahmed Fnaiech et al [20] proposed a method to increase the fear recognition rate by projecting visible images from 3D to 2D and using angle deviation. However, there was a limitation in performance comparison as the experiment only classified emotions into two categories: fear and other negative emotions.…”

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

“…Clearly, shadow occlusion problems cannot be solved well. The second technique uses lighting modeling and a 3D face [8][9][10][11] for recognition. Unfortunately, complex calculation limits its application.…”