A more effective way to label affective expressions

Eckhardt, Micah; Picard, Rosalind W.

doi:10.1109/acii.2009.5349528

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…The labels were subsequently labeled by another independent FACS trained individual (QA stage) and discrepancies within the coding reviewed (relabeling stage). For labeling we used a web-based, distributed video labeling system (ViDL) which is specifically designed for labeling affective data [6]. A version of ViDL developed by Affectiva was used for the labeling task.…”

Section: Facs Codingmentioning

confidence: 99%

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

McDuff

Kaliouby

Senechal

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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206

107

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Computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a benchmark resource. We present a comprehensively labeled dataset of ecologically valid spontaneous facial responses recorded in natural settings over the Internet. To collect the data, online viewers watched one of three intentionally amusing Super Bowl commercials and were simultaneously filmed using their webcam. They answered three self-report questions about their experience. A subset of viewers additionally gave consent for their data to be shared publicly with other researchers. This subset consists of 242 facial videos (168,359 frames) recorded in real world conditions. The dataset is comprehensively labeled for the following: 1) frame-by-frame labels for the presence of 10 symmetrical FACS action units, 4 asymmetric (unilateral) FACS action units, 2 head movements, smile, general expressiveness, feature tracker fails and gender; 2) the location of 22 automatically detected landmark points; 3) self-report responses of familiarity with, liking of, and desire to watch again for the stimuli videos and 4) baseline performance of detection algorithms on this dataset.

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Section: Facs Codingmentioning

confidence: 99%

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

McDuff

Kaliouby

Senechal

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Self Cite

206

107

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“…One example is VidL, a distributed video-labeling tool specifically designed for labeling affective data [9]. Another case is a "Guess What?"…”

Section: Crowd-sourcing Social Contextmentioning

confidence: 99%

“…One example is VidL, a distributed video‐labeling tool specifically designed for labeling affective data . Another case is a “Guess What?” game crowdsourcing affective video data labeling of social situations.…”

Section: Background and Related Workmentioning

confidence: 99%

Social context cognition crowd‐sourcing and semi‐automatic parametrization

Kochanowicz

Tan

Thalmann

2016

Computer Animation & Virtual

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This paper presents a semi-automatic method of parameterizing an existing social context cognition model. It discusses benefits of the social context cognition models for example in personality modeling and their key issue that is parametrization. It briefly introduces social context cognition model and describes a new method of its crowd-sourcing-based parametrization. Later, validation is provided, and ability to recreate social context cognition in the provided samples is presented with good generalization for the unknown cases. Finally, model's stability for the continuous stream of dynamic social context input data is shown. Presented system contributes to the believable agent modeling and social simulations by making much needed applications of social context cognition models easier by addressing the so far unsolved troublesome parametrization issues.

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“…Chen et al [2008] developed the EmoPlayer which has a similar user interface to the tool developed by Eckhardt and Picard [2009] but with a reversed functionality: it assists users to find specific scenes in a video sequence. Soleymani et al [2009] built a collaborative filtering system that retrieves video clips based on affective queries.…”

Section: Related Workmentioning

confidence: 99%

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

Tkalčič

Odi

et al. 2012

Lecture Notes in Computer Science

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Abstract. Affective labeling of multimedia content can be useful in recommender systems. In this paper we compare the effect of implicit and explicit affective labeling in an image recommender system. The implicit affective labeling method is based on an emotion detection technique that takes as input the video sequences of the users' facial expressions. It extracts Gabor low level features from the video frames and employs a kNN machine learning technique to generate affective labels in the valence-arousal-dominance space. We performed a comparative study of the performance of a content-based recommender (CBR) system for images that uses three types of metadata to model the users and the items: (i) generic metadata, (ii) explicitly acquired affective labels and (iii) implicitly acquired affective labels with the proposed methodology. The results showed that the CBR performs best when explicit labels are used. However, implicitly acquired labels yield a significantly better performance of the CBR than generic metadata while being an unobtrusive feedback tool.

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A more effective way to label affective expressions

Cited by 10 publications

References 2 publications

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

Social context cognition crowd‐sourcing and semi‐automatic parametrization

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

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A more effective way to label affective expressions

Cited by 10 publications

References 2 publications

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected &#x0022;In-the-Wild&#x0022;

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected &#x0022;In-the-Wild&#x0022;

Social context cognition crowd‐sourcing and semi‐automatic parametrization

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

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Product

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About

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"