Emotion Identification in Movies through Facial Expression Recognition

Almeida, João Emílio; Vilaça, Luís; Teixeira, Inês; Viana, Paula

doi:10.3390/app11156827

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Fourth, we had the challenge of getting clear grouped data of images captured in video clips since they are taken in non-standardised settings. To cope with this challenge, the use of facial landmarks alongside the original image is helpful [ 60 ]. It can act as an embedded regulation that can weigh facial expressions in the classification of emotions during skill-test situations.…”

Section: Study Strengths and Limitationsmentioning

confidence: 99%

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

et al. 2022

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Test anxiety and self-efficacy significantly influence the mastery of nursing skills. Facial expression recognition tools are central components to recognising these elements. This study investigated the frequent facial expressions conveyed by nursing students and examined the relationships between nursing skill mastery, test anxiety, self-efficacy, and facial expressions in a test-taking situation. Thirty-three second-year nursing students who were attending a university in a Korean metropolitan city participated. Test anxiety, self-efficacy, and facial expressions were collected while the students inserted indwelling catheters. Using Microsoft Azure software, the researchers examined the students’ facial expressions. Negative facial expressions, such as anger, disgust, sadness, and surprise, were more common during the test-taking situation than the practice trial. Fear was positively correlated with anxiety. None of the facial expressions had significant relationships with self-efficacy; however, disgust was positively associated with nursing skill mastery. The facial expressions during the practice and test-taking situations were similar; however, fear and disgust may have been indicators of test anxiety and skill mastery. To create a screening tool for detecting and caring for students’ emotions, further studies should explore students’ facial expressions that were not evaluated in this study.

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Section: Study Strengths and Limitationsmentioning

confidence: 99%

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

et al. 2022

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“…The loss function used is categorical cross entropy and optimizer for training used was Adam optimizer with a relatively slow learning rate. [14,15] The model was tried to train with Stochastic gradient descent algorithm but the training turned out to be too slow with a lower learning rate, and unstable with a higher learning rate. The training and validation loss and accuracy curves followed each other for a few epochs after which the validation loss was seen to be incrementally increasing which denoted that the model is overfitting beyond this point and couldn't get any better.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

Facial Emotion Classifier using Convolutional Neural Networks for Reaction Review

Madhavi,

Gujar,

Jadhao

et al. 2022

ITM Web Conf.

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Applications of facial emotion classification is gaining popularity in the world. There are many ways to train a model to classify human facial expressions by use of existing technologies. The strategy to order and recognize feelings of an individual conveyed by his facial expression is done by contrasting it to a gathered set of labelled experiences of feelings. In this paper, we propose the making of an intelligent system that will recognize and classify facial emotions. A multi-layer Convolutional Neural Network model is proposed. Another method of training using pretrained ResNet50 Model is explored. A basic live video streaming application is developed to showcase the use case of our model which will be capable of monitoring and recording facial emotions in real time from a live video stream and subsequently summarize the overall reactions at the end of the stream.

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“…Physiological signals such as electroencephalogram ( 11 , 12 ) and features from eye-blinking ( 13 ) were captured upon audio-visual stimuli to classify emotions by utilizing deep neural networks. More common approaches include applying deep learning models on audio and visual data from clinical patients and public datasets ( 14 , 15 ), where widely used datasets classified facial expressions into emotional labels such as anger, disgust, fear, happiness, sadness, surprise, and neutral ( 16 ). Symptom severity of depression was measured based on the speech and 3D facial scan data in DAIC-WOZ dataset, and the convolutional neural network (CNN) model was reported to demonstrate reliable results in detecting MDD ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Development of depression detection algorithm using text scripts of routine psychiatric interview

Oh,

Lee,

Chung

et al. 2024

Front. Psychiatry

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BackgroundA psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient’s medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient’s emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients.MethodsSeventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject’s representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts.ResultsA machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001).ConclusionThis is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.

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Emotion Identification in Movies through Facial Expression Recognition

Cited by 12 publications

References 43 publications

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

Facial Emotion Classifier using Convolutional Neural Networks for Reaction Review

Development of depression detection algorithm using text scripts of routine psychiatric interview

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