Enhanced Autocorrelation in Real World Emotion Recognition

Meudt, Sascha; Schwenker, Friedhelm

doi:10.1145/2663204.2666276

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…Nevertheless, in this paper we focus on methodologies that are based on neural networks since they achieved the best performance. The interested reader may refer to [14,3,61,33,9,60,71,40,84] for further details.…”

Section: Deep Learning Methodologies For Facial Expression Recognitiomentioning

confidence: 99%

Facial Affect “In-the-Wild”: A Survey and a New Database

Zafeiriou

Papaioannou

Kotsia

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in-the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour inthe-wild, namely the datasets and methodologies that have been developed thus far, while paying particular attention to recently proposed deep learning techniques. Finally, we attempt a significant step further by proposing a novel, comprehensive benchmark that can be utilized for evaluating and training various methodologies for the problems of facial affect, behaviour analysis and understanding "inthe-wild". To the best of our knowledge, this is the first benchmark proposed for measuring continuous affect in the valence-arousal space "in-the-wild".

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Section: Deep Learning Methodologies For Facial Expression Recognitiomentioning

confidence: 99%

Facial Affect “In-the-Wild”: A Survey and a New Database

Zafeiriou

Papaioannou

Kotsia

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…An interesting future line of research is to experiment with novel sensing technologies to capture behavioral data (physiological data, facial expressions, speech, ...) in order to detect emotions and to adapt recommendations based on emotional responses. Although measurement of emotions in a controlled laboratory environment is well studied for years by a large number of research groups [20], multimodal emotion recognition in real world environments is still a challenging task [17]. A good review of existing methods has been reported by Hrabal [13].…”

Section: Adaptivementioning

confidence: 99%

HCI for Recommender Systems

Valdez

Ziefle

Verbert

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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How can you discover something new, that matches your interest? Recommender Systems have been studied since the 90ies. Their benefit comes from guiding a user through the density of the information jungle to useful knowledge clearings. Early research on recommender systems focuses on algorithms and their evaluation to improve recommendation accuracy using F-measures and other methodologies from signal-detection theory. Present research includes other aspects such as human factors that affect the user experience and interactive visualization techniques to support transparency of results and user control. In this paper, we analyze all publications on recommender systems from the scopus database, and particularly also papers with such an HCI focus. Based on an analysis of these papers, future topics for recommender systems research are identified, which include more advanced support for user control, adaptive interfaces, affective computing and applications in high risk domains.

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