Robust Feature Detection for Facial Expression Recognition

Ioannou, Spiros; Caridakis, George; Karpouzis, Kostas; Kollias, Stefanos

doi:10.1155/2007/29081

Cited by 35 publications

(14 citation statements)

References 30 publications

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“…This approach minimizes the search area for facial feature boundaries into a small proportion of the entire image, thus speeding up the feature extraction process. For every facial feature, a multicue approach is adopted, generating a number of masks which are produced by a number of algorithms [42] performing well under different lighting conditions and resolutions. Feature masks generated for each facial feature are fused together to produce the final mask for that feature.…”

Section: Face Feature Extractionmentioning

confidence: 99%

“…In an attempt to validate the proposed facial feature extraction algorithm, 250 frames (randomly selected) were manually annotated from two human observers and the group agreement metric was calculated [42]. This metric was the Williams's Index (WI), which actually divides the average number of agreements (inverse disagreements) between the computer (observer 0) and human observers by the average number of agreements between human observers.…”

Section: Face Feature Extractionmentioning

confidence: 99%

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Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Kessous¹,

Castellano

2009

J Multimodal User Interfaces

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205

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In this paper a study on multimodal automatic emotion recognition during a speech-based interaction is presented. A database was constructed consisting of people pronouncing a sentence in a scenario where they interacted with an agent using speech. Ten people pronounced a sentence corresponding to a command while making 8 different emotional expressions. Gender was equally represented, with speakers of several different native languages including French, German, Greek and Italian. Facial expression, gesture and acoustic analysis of speech were used to extract features relevant to emotion. For the automatic classification of unimodal data, bimodal data and multimodal data, a system based on a Bayesian classifier was used. After performing an automatic classification of each modality, the different modalities were combined using a multimodal approach. Fusion of the modalities at the feature level (before running the classifier) and at the results level (combining results from classifier from each modality) were compared. Fusing the multimodal data resulted in a large increase in the recognition rates in comparison to the unimodal systems: the multimodal approach increased the recognition rate by more than 10% when compared to the most successful unimodal system. Bimodal emotion recognition based on all combinations of the modalities (i.e., 'face-gesture', 'facespeech' and 'gesture-speech') was also investigated. The results show that the best pairing is 'gesture-speech'. Using all three modalities resulted in a 3.3% classification improvement over the best bimodal results.

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Section: Face Feature Extractionmentioning

confidence: 99%

Section: Face Feature Extractionmentioning

confidence: 99%

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Kessous¹,

Castellano

2009

J Multimodal User Interfaces

Self Cite

205

View full text Add to dashboard Cite

show abstract

“…Although physiological measurement devices have been used extensively within the affective computing research for emotion recognition [17] and despite the great efforts that have been devoted to make these devices wearable, their use in commercial computer games is still limited. Facial expressions [7] and head movements [1] are rich non intrusive sources of information regarding the issue of capturing the emotional or behavioral state of a person while interacting with a machine or undertaking certain tasks. A lot of work has been done in recent bibliography for modeling such cues in a variety of environments.…”

Section: Introductionmentioning

confidence: 99%

A Game-Based Corpus for Analysing the Interplay between Game Context and Player Experience

Shaker

Asteriadis

Yannakakis

et al. 2011

Affective Computing and Intelligent Interaction

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Abstract. Recognizing players' affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players' behavior while playing video games as a case study of the possible use of the corpus.

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“…For the detection of the eye corners (left, right, upper and lower) a technique similar to that described in [15] is used: Having found the eye center, a small area around it is used for the rest of the points to be detected. This is done by using the Generalized Projection Functions (GPFs) which are a combination of the Integral Projection Functions (IPFs) and the Variance Projection Functions (VPFs).…”

Section: Detection and Tracking Of Facial Features -Gaze And Pose Estmentioning

confidence: 99%

Non-Verbal Feedback on User Interest Based on Gaze Direction and Head Pose

Asteriadis

Tzouveli

Karpouzis

et al. 2007

Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)

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Robust Feature Detection for Facial Expression Recognition

Cited by 35 publications

References 30 publications

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

A Game-Based Corpus for Analysing the Interplay between Game Context and Player Experience

Non-Verbal Feedback on User Interest Based on Gaze Direction and Head Pose

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