Emotion Recognition from 2D Facial Expressions

Taha, Bilal; Hatzinakos, Dimitrios

doi:10.1109/ccece.2019.8861751

Cited by 18 publications

(7 citation statements)

References 10 publications

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“…This project is about developing a clinical depression assistant which gives depression severity status based on Heart Rate Variability [12], Cyclic Alternation Pattern [13] and Random Eye Movement [14] of sleep, Static facial features [15] and Dynamic facial features [16] of a patient. The stated patient data access from the patient's fitness tracker and Camera of a smartphone via developed application.…”

Section: Methodsmentioning

confidence: 99%

Dheergayu: Clinical Depression Monitoring Assistant

A.A.M.R¹,

K.G.T.D²,

N.I.R³

et al. 2020

GJCST

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Depression is identified as one of the most common mental health disorders in the world. Depression not only impacts the patient but also their families and relatives. If not properly treated, due to these reasons it leads people to hazardous situations. Nonetheless existing clinical diagnosis tools for monitoring illness trajectory are inadequate. Traditionally, psychiatrists use one to one interaction assessments to diagnose depression levels. However, these clinic- centered services can pose several operational challenges. In order to monitor clinical depressive disorders, patients are required to travel regularly to a clinical center within its limited operating hours. These procedures are highly resource intensive because they require skilled clinician and laboratories. To address these issues, we propose a personal and ubiquitous sensing technologies, such as fitness trackers and smartphones, which can monitor human vitals in an unobtrusive manner.

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Section: Methodsmentioning

confidence: 99%

Dheergayu: Clinical Depression Monitoring Assistant

A.A.M.R¹,

K.G.T.D²,

N.I.R³

et al. 2020

GJCST

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show abstract

“…Facial expression recognition and AU detection have been investigated primarily using 2D data [ 19 , 20 ], and there are not many works addressing AU detection from 3D scans. Despite AUs are activated by facial muscles, which implies a deformation of the 3D face surface, their definition and detection are based on the appearance changes that such movements induce on the texture.…”

Section: Related Workmentioning

confidence: 99%

Action Unit Detection by Learning the Deformation Coefficients of a 3D Morphable Model

Ariano

Ferrari

Bimbo

2021

Sensors

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Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and analyzing AUs in 3D is particularly relevant for the potential applications it can enable. In this paper, we propose a solution for 3D AU detection and synthesis by developing on a newly defined 3D Morphable Model (3DMM) of the face. Differently from most of the 3DMMs existing in the literature, which mainly model global variations of the face and show limitations in adapting to local and asymmetric deformations, the proposed solution is specifically devised to cope with such difficult morphings. During a training phase, the deformation coefficients are learned that enable the 3DMM to deform to 3D target scans showing neutral and facial expression of the same individual, thus decoupling expression from identity deformations. Then, such deformation coefficients are used, on the one hand, to train an AU classifier, on the other, they can be applied to a 3D neutral scan to generate AU deformations in a subject-independent manner. The proposed approach for AU detection is validated on the Bosphorus dataset, reporting competitive results with respect to the state-of-the-art, even in a challenging cross-dataset setting. We further show the learned coefficients are general enough to synthesize realistic 3D face instances with AUs activation.

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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 the work of Anjith et al [2], the authors employed a multi-channel convolutional neural network (CNN) structure to achieve better performance when compared to baseline techniques. Taha and Hatzinakos [3] developed an approach to detect and learn informative representations from 2D gray-level images or FER using a CNN structure. They utilized a few layers strategy to mull over the overfitting issue.…”

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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Emotion Recognition from 2D Facial Expressions

Cited by 18 publications

References 10 publications

Dheergayu: Clinical Depression Monitoring Assistant

Dheergayu: Clinical Depression Monitoring Assistant

Action Unit Detection by Learning the Deformation Coefficients of a 3D Morphable Model

Fear Facial Emotion Recognition Based on Angular Deviation

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