Quantitative comparison of motion history image variants for video-based depression assessment

Pampouchidou, Anastasia; Pediaditis, Matthew; Maridaki, Anna; Awais, Muhammad; Vazakopoulou, Calliope-Marina; Sfakianakis, Stelios; Tsiknakis, Manolis; Simos, Panagiotis G.; Marias, Kostas; Yang, Fan; Mériaudeau, Fabrice

doi:10.1186/s13640-017-0212-3

Cited by 19 publications

(3 citation statements)

References 35 publications

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“…Additional studies have detected attention deficit hyperactivity disorder from gestures and body movements [367]. Such differences have been used to diagnose mental health problems [375], [376]. See Table 7 for more details about differentiating clinical disorders from healthy controls.…”

Section: Emotion and Mental Healthmentioning

confidence: 99%

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Wang

Zhao

et al. 2023

Proc. IEEE

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The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of “emotion,” coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a “Holy Grail” research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

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Section: Emotion and Mental Healthmentioning

confidence: 99%

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Wang

Zhao

et al. 2023

Proc. IEEE

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“…A number of roadblocks, however, are hindering the development of these technologies. Deep emotion analysis algorithms are often based on very short consecutive segments, often just a single frame [42]. Therefore, the prediction of behavior can be quite inaccurate.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Emotions Recognition from Facial Expressions using Deep Learning

Shahzadi,

Fuzail,

Aslam

2023

VFAST trans. softw. eng.

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Deep emotion recognition has a wide range of applications, including human-robot communication, business, movies, services hotels, and even politics. Despite the use of various supervised and unsupervised methods in many different fields, there is still a lack of accurate analysis. Therefore, we have taken on this challenge as our research problem. We have proposed a mechanism for efficient and fine-grained classification of human deep emotions that can be applied to many other problems in daily life. This study aims to explore the best-suited algorithm along with optimal parameters to provide a solution for an efficient emotion detection machine learning system. In this study, we aimed to recognize emotions from facial expressions using deep learning techniques and the JAFFE dataset. The performance of three different models, a CNN (Convolutional Neural Network), an ANN (Artificial Neural Network), and an SVM (Support Vector Machine) were evaluated using precision, recall, F1-score, and accuracy as the evaluation metrics. The results of the experiments show that all three models performed well in recognizing emotions from facial expressions. The CNN model achieved a precision of 0.653, recall of 0.561, F1-score of 0.567, and accuracy of 0.62. The ANN model achieved a precision of 0.623, recall of 0.542, F1-score of 0.542, and accuracy of 0.59. The SVM model achieved a precision of 0.643, recall of 0.559, F1-score of 0.545, and accuracy of 0.6. Overall, the results of the study indicate that deep learning techniques can be effectively used for recognizing emotions from facial expressions using the JAFFE dataset.

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“…Based on the review of the literature for depression detection, the techniques suggested can be categorized as unimodal and multi-modal. Uni-modal techniques are the ones that involve judgement/analysis based on a single modality: only video [2][3][4], only text [5][6][7], or only speech [8][9][10]. Multi-modal techniques, which are a hybrid of two or more modalities (videos, speech, and text) together [11][12][13] have been proposed to improve the performance further.…”

Section: Introductionmentioning

confidence: 99%

Optimal Combination of Multivariate Filter Feature Selection and Classifier for Speech-Based Depression Detection

Sharma

Bustamante

2021

Artificial Intelligence Evolution

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In this paper, we have focused to improve the performance of a speech-based uni-modal depression detection system, which is non-invasive, involves low cost and computation time in comparison to multi-modal systems. The performance of a decision system mainly depends on the choice of feature selection method and the classifier. We have investigated the combination of four well-known multivariate filter methods (minimum Redundancy Maximum Relevance, Scatter Ratio, Mahalanobis Distance, Fast Correlation Based feature selection) and four well-known classifiers (k-Nearest Neighbour, Linear Discriminant classifier, Decision Tree, Support Vector Machine) to obtain a minimal set of relevant and non-redundant features to improve the performance. This will speed up the acquisition of features from speech and build the decision system with low cost and complexity. Experimental results on the high and low-level features of recent work on the DAICWOZ dataset demonstrate the superior performance of the combination of Scatter Ratio and LDC as well as that of Mahalanobis Distance and LDC, in comparison to other combinations and existing speech-based depression results, for both gender independent and gender-based studies. Further, these combinations have also outperformed a few multimodal systems. It was noted that low-level features are more discriminatory and provide a better f1 score.

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Quantitative comparison of motion history image variants for video-based depression assessment

Cited by 19 publications

References 35 publications

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Deep Emotions Recognition from Facial Expressions using Deep Learning

Optimal Combination of Multivariate Filter Feature Selection and Classifier for Speech-Based Depression Detection

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