Modeling Latent Discriminative Dynamic of Multi-dimensional Affective Signals

Ramirez, Geovany A.; Baltrušaitis, Tadas; Morency, Louis–Philippe

doi:10.1007/978-3-642-24571-8_51

Cited by 71 publications

(57 citation statements)

References 18 publications

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“…Dahmane et al [2] use Gabor filter energies to compute their visual features. Ramirez et al [15], conversely, prefer to extract high-level features such as gaze direction, head tilt or smile intensity. Similarly, Gunes et al [6] focus on spontaneous head movements.…”

Section: Introductionmentioning

confidence: 99%

Robust continuous prediction of human emotions using multiscale dynamic cues

Nicolle

Rapp

Bailly

et al. 2012

Proceedings of the 14th ACM International Conference on Multimodal Interaction

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Designing systems able to interact with humans in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the Audio/Visual Emotion Challenge (AVEC'12) whose goal is to continuously predict four affective signals describing human emotions (namely valence, arousal, expectancy and power). The proposed method uses log-magnitude Fourier spectra to extract multiscale dynamic descriptions of signals characterizing global and local face appearance as well as head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. For selecting features, we introduce a new correlation-based measure that takes into account a possible delay between the labels and the data and significantly increases robustness. We also propose a particularly fast regressor-level fusion framework to merge systems based on di↵erent modalities. Experiments have proven the e ciency of each key point of the proposed method and we obtain very promising results.

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Section: Introductionmentioning

confidence: 99%

Robust continuous prediction of human emotions using multiscale dynamic cues

Nicolle

Rapp

Bailly

et al. 2012

Proceedings of the 14th ACM International Conference on Multimodal Interaction

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“…In recent years, the field of dimensional continuous emotion analysis has gained rising attention, and a significant number of works has been published on this topic [2,3,12,10]. Introduced by Russel [11], this emotion description originated a radically different approach on describing emotional states.…”

Section: Introductionmentioning

confidence: 99%

Correlated-spaces regression for learning continuous emotion dimensions

Nicolaou

Zafeiriou

Pantić

2013

Proceedings of the 21st ACM International Conference on Multimedia

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Adopting continuous dimensional annotations for affective analysis has been gaining rising attention by researchers over the past years. Due to the idiosyncratic nature of this problem, many subproblems have been identified, spanning from the fusion of multiple continuous annotations to exploiting output-correlations amongst emotion dimensions. In this paper, we firstly empirically answer several important questions which have found partial or no answer at all so far in related literature. In more detail, we study the correlation of each emotion dimension (i) with respect to other emotion dimensions, (ii) to basic emotions (e.g., happiness, anger). As a measure for comparison, we use video and audio features. Interestingly enough, we find that (i) each emotion dimension is more correlated with other emotion dimensions rather than with face and audio features, and similarly (ii) that each basic emotion is more correlated with emotion dimensions than with audio and video features. Motivated by these findings, we present a novel regression algorithm (Correlated-Spaces Regression, CSR), inspired by Canonical Correlation Analysis (CCA) which learns outputcorrelations and performs supervised dimensionality reduction and multimodal fusion by (i) projecting features extracted from all modalities and labels onto a common space where their inter-correlation is maximised and (ii) learning mappings from the projected feature space onto the projected, uncorrelated label space.

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“…For AVEC 2011: UCL (Meng and Bianchi-Berthouze 2011), Uni-ULM (Glodek et al 2011), GaTechKim (Kim et al 2011), LSU (Calix et al 2011), Waterloo (Sayedelahl et al 2011), NLPR (Pan et al 2011), USC (Ramirez et al 2011), GaTechSun (Sun and Moore 2011), I2R-SCUT (Cen et al 2011), UCR (Cruz et al 2011) and UMontreal (Dahmane and Meunier 2011a, b). For AVEC 2012: UPMC-UAG (Nicolle et al 2012), Supelec-Dynamixyz-MinesTelecom (Soladie et al 2012), UPenn (Savran et al 2012a), USC (Ozkan et al 2012), Delft (van der Maaten 2012), Uni-ULM (Glodek et al 2012), Waterloo2 (Fewzee and Karray 2012).…”

Section: Audio/visual Emotion Challenge 2011/2012mentioning

confidence: 99%

Automatic Facial Expression Analysis

Valstar

2014

Understanding Facial Expressions in Communication

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Modeling Latent Discriminative Dynamic of Multi-dimensional Affective Signals

Cited by 71 publications

References 18 publications

Robust continuous prediction of human emotions using multiscale dynamic cues

Robust continuous prediction of human emotions using multiscale dynamic cues

Correlated-spaces regression for learning continuous emotion dimensions

Automatic Facial Expression Analysis

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