Facial Action Recognition Combining Heterogeneous Features via Multikernel Learning

Senechal, Thibaud; Rapp, Vincent; Salam, Hanan; Séguier, Renaud; Bailly, Kévin; Prévost, Lionel

doi:10.1109/tsmcb.2012.2193567

Cited by 111 publications

(63 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Meta-analysis of challenge results are summarized in [21]. These methods generally use discrete systems whether based on static descriptors (geometrical or appearance features) and/or on static classifiers such as Support Vector Machines [20].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…As input to our model, we used both geometric features, i.e., the registered facial points (feature set I), and appearance features, i.e., local binary patterns (LBP) histograms [61] (feature set II) extracted around each facial point from a region of 32×32 pixels. We chose these features as they showed good performance in variety of AU recognition tasks [10]. To reduce the dimensionality of the extracted features we applied PCA, retaining 95% of the energy.…”

Section: B Experimental Settingsmentioning

confidence: 99%

“…Others do not exhibit strong cooccurrence patterns (e.g., AU5 in DISFA). Hence, we selected the following subsets of highly correlated AUs: AUs (1,2,4,6,7,12,15,17) for CK+, AUs (1,2,4,6,12,15,17) for DISFA and AUs (4,6,7,9,10,43) for Shoulder-pain. The selected AUs occur jointly in the context of recorded expressions (e.g., pain expression, see [58]).…”

Section: B Experimental Settingsmentioning

confidence: 99%

“…Despite all this, most of the existing approaches to AU detection model each AU independently, using either a single type of facial features [7], [8], or combining multiple features by means of naive approach (i.e., simple feature concatenation) [4], [9] or multiple-kernel learning (MKL) [10]. Furthermore, some methods treat different combinations of AUs as new independent classes [11]; however, this is impractical given the number of possible AU combinations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach

Eleftheriadis

Rudovic

Pantić

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

show abstract

“…The approaches used in such system are Active Appearance Model (AAM) [5], manually locating a number of facial points. And recently, Piecewise Bezier Volume Deformation tracker (PBVD) was proposed [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Facial Expression Recognition via Sparse Representation and Multiple Gabor filters

El-Sayed¹,

Kholy²,

El-Nahas³

2013

IJACSA

View full text Add to dashboard Cite

Abstract-Facial expressions recognition plays important role in human communication. It has become one of the most challenging tasks in the pattern recognition field. It has many applications such as: human computer interaction, video surveillance, forensic applications, criminal investigations, and in many other fields. In this paper we propose a method for facial expression recognition (FER). This method provides new insights into two issues in FER: feature extraction and robustness. For feature extraction we are using sparse representation approach after applying multiple Gabor filter and then using support vector machine (SVM) as classifier. We conduct extensive experiments on standard facial expressions database to verify the performance of proposed method. And we compare the result with other approach.

show abstract

Facial Action Recognition Combining Heterogeneous Features via Multikernel Learning

Cited by 111 publications

References 49 publications

Robust continuous prediction of human emotions using multiscale dynamic cues

Robust continuous prediction of human emotions using multiscale dynamic cues

Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach

Robust Facial Expression Recognition via Sparse Representation and Multiple Gabor filters

Contact Info

Product

Resources

About