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Meta-Analysis of the First Facial . . .
, 2002
"... . . . an active topic in computer science for over two decades, in particular Facial Action Coding System (FACS) Action Unit ..."
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. . . an active topic in computer science for over two decades, in particular Facial Action Coding System (FACS) Action Unit
Vision and Attention Theory Based Sampling for Continuous Facial Emotion Recognition
"... Abstract—Affective computing—the emergent field in which computers detect emotions and project appropriate expressions of their own—has reached a bottleneck where algorithms are not able to infer a person’s emotions from natural and spontaneous facial expressions captured in video. While the field o ..."
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Abstract—Affective computing—the emergent field in which computers detect emotions and project appropriate expressions of their own—has reached a bottleneck where algorithms are not able to infer a person’s emotions from natural and spontaneous facial expressions captured in video. While the field of emotion recognition has seen many advances in the past decade, a facial emotion recognition approach has not yet been revealed which performs well in unconstrained settings. In this paper, we propose a principled method which addresses the temporal dynamics of facial emotions and expressions in video with a sampling approach inspired from human perceptual psychology. We test the efficacy of the method on the Audio/Visual Emotion Challenge 2011 and 2012, Cohn-Kanade and the MMI Facial Expression Database. The method shows an average improvement of 9.8 percent over the baseline for weighted accuracy on the Audio/Visual Emotion Challenge 2011 video-based frame-level subchallenge testing set. Index Terms—Facial expressions, audio/visual emotion challenge, sampling and interpolation Ç 1
Learning Temporal Alignment Uncertainty for Efficient Event Detection
"... Abstract—In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and ef-ficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a ..."
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Abstract—In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and ef-ficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks. I.
in the Facial Expression Recognition and Analysis (FERA2015)
"... Abstract — This article describes a system for participation ..."
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tional challenge on Facial Emotion Recognition and Analysis. We
"... Abstract—This paper presents our response to the first interna- ..."
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Contemporary Challenges for a Social Signal processing
"... This paper provides a short overview of Social Signal Processing. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ..."
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This paper provides a short overview of Social Signal Processing. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ignoring a crucial range of abilities that matter immensely for how people do in life. This range of abilities is called social intelligence and includes the ability to express and recognize social signals produced during social interactions like agreement, politeness, empathy, friendliness, conflict, etc., coupled with the ability to manage them in order to get along well with others while winning their cooperation. Social Signal Processing (SSP) is the new research domain that aims at understanding and modeling social interactions (human-science goals), and at providing computers with similar abilities in human-computer interaction scenarios (technological goals). SSP is in its infancy and the journey towards artificial social intelligence and socially-aware computing is still long, the paper outlines its future perspectives and some of its most promising applications.
