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:10.48550/arxiv.1702.02510

Cited by 4 publications

(7 citation statements)

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“…Big Five personality traits are usually analyzed from lifelog data or questionnaires [40]. Here we show the possibility of determining personality traits from 2-dimensional affective components.…”

Section: Emotion-to-personality Relationshipmentioning

confidence: 98%

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Zhang

Peng

Winkler

2022

IEEE Trans. Affective Comput.

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Personality and emotion are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network which we call emotion network and personality network, respectively. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. An adversarial-like loss function is further employed to promote representation coherence among heterogeneous dataset sources. Based on this, the emotion-to-personality relationship is also well explored. Extensive experiments are provided to demonstrate the effectiveness of PersEmoN.

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Section: Emotion-to-personality Relationshipmentioning

confidence: 98%

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Zhang

Peng

Winkler

2022

IEEE Trans. Affective Comput.

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“…Our current study has some limitations, e.g., we only considered one dataset, and the dimensionality of the features was not high enough. We will deal with them in our future research, by considering more affective computing datasets, e.g., MAHNOB-HCI [36], MSP-IMPROV [1], and AMIGOS [28], and by extracting more features, e.g., through OpenSMILE [6]. Additionally, we will:…”

Section: Resultsmentioning

confidence: 99%

Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study

Guo

2018

2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

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Affective computing has become a very important research area in human-machine interaction. However, affects are subjective, subtle, and uncertain. So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract. Thus, dimensionality reduction is critical in affective computing. This paper presents our preliminary study on dimensionality reduction for affect classification. Five popular dimensionality reduction approaches are introduced and compared. Experiments on the DEAP dataset showed that no approach can universally outperform others, and performing classification using the raw features directly may not always be a bad choice.

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“…This study uses AMIGOS Dataset [7] for designing and evaluating our framework. This dataset has 40 subjects watching 16 short (51-150 seconds) video clips (total 640 video trials).…”

Section: Dataset Desciption and Proposed Methodsmentioning

confidence: 99%

“…The PSD for every second is then averaged over the length of the video. The raw PSD values so obtained do not work very well in the emotion-classification problem [7]. Hence, we tried to use convolutional neural networks (CNNs) for extracting more useful features based on the PSD values.…”

Section: A Eeg-based Extractionmentioning

confidence: 99%

Multi-modal Approach for Affective Computing

Siddharth

Jung

Sejnowski

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these studies are constrained by the limitations of these modalities such as the absence of physiological biomarkers in the face-video analysis, poor spatial resolution in EEG, poor temporal resolution of the GSR etc. Scant research has been conducted to compare the merits of these modalities and understand how to best use them individually and jointly. Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities. Using a novel method for compensating physiological baseline we show an increase in the classification accuracy of various approaches that we use. Finally, we present a multi-modal emotion-classification approach in the domain of affective computing research.

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

Cited by 4 publications

References 0 publications

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study

Multi-modal Approach for Affective Computing

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