AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

Miranda-Correa, Juan Abdon; Abadi, Mojtaba Khomami; Sebe, Nicu; Patras, Ioannis

doi:10.1109/taffc.2018.2884461

Cited by 393 publications

(293 citation statements)

References 54 publications

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“…These results are indicative of the fact that neural responses, which represent an implicit manifestation of emotional perception/expression, best reflect explicit affective impressions reported by humans. It is therefore unsurprising that a large number of recent affect prediction approaches [14]- [16], [56] have employed neural sensing as one of the modalities incorporating emotional information.…”

Section: User Study Resultsmentioning

confidence: 99%

Recognition of Advertisement Emotions With Application to Computational Advertising

Shukla

Gullapuram

Katti

et al. 2022

IEEE Trans. Affective Comput.

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Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience. However, most computational affect recognition (AR) approaches examine ads via the text modality, and only limited work has been devoted to decoding ad emotions from audiovisual or user cues. This work (1) compiles an affective ad dataset capable of evoking coherent emotions across users; (2) explores the efficacy of content-centric convolutional neural network (CNN) features for AR vis-ã-vis handcrafted audio-visual descriptors; (3) examines user-centric ad AR from Electroencephalogram (EEG) responses acquired during ad-viewing, and (4) demonstrates how better affect predictions facilitate effective computational advertising as determined by a study involving 18 users. Experiments reveal that (a) CNN features outperform audiovisual descriptors for content-centric AR; (b) EEG features are able to encode ad-induced emotions better than content-based features; (c) Multi-task learning performs best among a slew of classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG features also enable optimized ad insertion onto streamed video, as compared to contentbased or manual insertion techniques in terms of ad memorability and overall user experience.

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Section: User Study Resultsmentioning

confidence: 99%

Recognition of Advertisement Emotions With Application to Computational Advertising

Shukla

Gullapuram

Katti

et al. 2022

IEEE Trans. Affective Comput.

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“…1-4, high arousal: 6-9; negative valence: 1-4; positive valence: 6-9) were normalized to a scale from -1 to 1 (Miranda Correa, Abadi, Sebe, & Patras, 2018). Data points with 0 valence or 0 arousal were deleted.…”

Section: Resultsmentioning

confidence: 99%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers' takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers' takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.

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“…Instead, they employ 'static' classifiers that process global features from the input time series (or for a small number of segments). Such approaches include Naive Bayes (NB) [40], [41], linear discriminant analysis (LDA) [42], and support vector machine (SVM) [43], [44], [45]. A summary can be found in Table I. A number of studies have sought to model temporal information within EEG signals, using hidden Markov models [46], Gaussian Process models [47], continuous conditional random fields [48], and long short-term memory (LSTM) neural networks [49].…”

Section: A Unimodal Heartbeat and Temporal Modelsmentioning

confidence: 99%

“…Moreover, real-world applications may not permit subject-specific calibration, making models that can generalise to new individuals necessary. We propose that a sensible evaluation method is leave-k-subjects-out (LkSO) cross-validation, which has been used previously [40], [52], [42] and will be adopted in this study.…”

Section: A Unimodal Heartbeat and Temporal Modelsmentioning

confidence: 99%

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Harper

Southern

2022

IEEE Trans. Affective Comput.

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Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities − audio, visual, and physiological − to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in realworld domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.

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AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

Cited by 393 publications

References 54 publications

Recognition of Advertisement Emotions With Application to Computational Advertising

Recognition of Advertisement Emotions With Application to Computational Advertising

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

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