Emotion Sensing From Head Motion Capture

Samanta, Atanu; Guha, Tanaya

doi:10.1109/jsen.2020.3033431

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…The participants were also allowed to provide the selfassessment records through a questionnaire asked based on the given video's social context, such as familiarity, like the video or not. On the other hand, emotional affect [18] and its dimensions are notified such as anger, sadness, happy, excitement etc. The Gaussian mixture model acts as the probabilistic clustering of data into various nodes and formulates the pattern segregation based on the statistical equivalents of the frames of data.…”

Section: Methodology 31 System Architecturementioning

confidence: 99%

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Bakkialakshmi

SudalaiMuthu

2022

Bulletin EEI

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Affective computing is the study of the deep extraction of emotional impacts that triggers humans for various reasons. Emotions directly reflect on human behaviour. The proposed analysis is inclined towards deep emotion extraction through a novel concept with less computation time. Designing a robust analysis model is focused on here. AMIGOS dataset on affect, and personality modelling is considered here. A Novel Gaussian ResiNet (GRN) algorithm is evaluated here. Any changes in the emotions of humans are the brainy response given to the actions faced. The features of the given physiological factors are considered for analysis, further with GMM-ResiNet (GRN) a low computational structure is used for classification. The Novel Gaussian ResiNet (GRN) is created from the given dataset for similar feature validations. The system predicts the correlated relative data from the training set and testing set and achieved the performance metrics using error rate (ER), Algorithm Computation Time (ACT), Full Computation Time (FCT), Accuracy (AUC) etc. Novel Gaussian ResiNet (GRN) is created and tested with processed data of the AMIGOS dataset. The model created is validated with state-of-art approaches and achieved an accuracy of 92.6%.

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Section: Methodology 31 System Architecturementioning

confidence: 99%

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Bakkialakshmi

SudalaiMuthu

2022

Bulletin EEI

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“…Head motion in human-centered traits: In the context of affect analysis, head motion patterns have been used to study coordination between mothers and infants [14,15], emotion recognition [30], measuring engagement levels of dementia patients [27] and for analyzing interpersonal coordination in couple therapy [13,38].…”

Section: Head Motion For Behavioral Analyticsmentioning

confidence: 99%

“…Past work on motion modeling has focused on extracting head-motion patterns such as nods and shakes [12], or learning arbitrary head gestures [39] with no physical meaning. Differently, we unsupervisedly learn meaningful motion patterns following [30] to translate head motion into a sequence of kinemes.…”

Section: Kineme Formulationmentioning

confidence: 99%

“…Given the head-motion time-series extracted from a set of videos, we divide each time-series 𝜽 into short overlapping segments of length ℓ (overlapping segments enable shift-invariance, are empirically found to generate better representations in [30]). The 𝑖 𝑡ℎ segment is denoted by a vector h…”

Section: Head Motion As Kineme Sequencementioning

confidence: 99%

“…The above examples convey that head-motion plays a critical role in conveying personality impressions. Recently, kinemes denoting elementary head-motion units akin to phonemes in human speech [3], are shown to be effective emotional cues [30]. Apart from enabling human-comparable recognition performance, kinemes also allow for explanations relating to predictions; e.g., the sad emotion is associated with slow head movements [10].…”

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confidence: 99%

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Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics

Madan

Gahalawat

Guha

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits. Transforming head-motion patterns into a sequence of kinemes facilitates discovery of latent temporal signatures characterizing the targeted traits, thereby enabling both efficient and explainable trait prediction. Utilizing Kinemes and Facial Action Coding System (FACS) features to predict (a) OCEAN personality traits on the First Impressions Candidate Screening videos, and (b) Interview traits on the MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained with kineme sequences performs better than or similar to a Convolutional Neural Network (CNN) trained with facial images; (2) Accurate predictions and explanations are achieved on combining FACS action units (AUs) with kinemes, and (3) Prediction performance is affected by the time-length over which head and facial movements are observed. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Computer vision representations.

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Application of animation products via multimodal information and semantic analogy

Chu

2023

Multimed Tools Appl

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Emotion Sensing From Head Motion Capture

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 7 publications

References 26 publications

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics

Application of animation products via multimodal information and semantic analogy

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