Audiovisual Behavior Modeling by Combined Feature Spaces

Schuller, Björn; Arsic, D.; Rigoll, Gerhard; Wimmer, Matthias; Radig, Bernd

doi:10.1109/icassp.2007.366340

Cited by 64 publications

(33 citation statements)

References 9 publications

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“…The proposed algorithm was evaluated on the basis of the Airplane Behavior Corpus (ABC) that was presented in [15]. The corpus contains a total of 11.5 h video.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Robust tracking of facial feature points with 3D Active Shape Models

Kaiser

Arsic

Sural

et al. 2010

2010 IEEE International Conference on Image Processing

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Exact 3D tracking of facial feature points is appealing for many applications in human-machine interaction. In this work a 3D Active Shape Model (ASM) that can be shifted, scaled, and rotated is used to track the points. The efficient Gauss-Newton method is applied to estimate the 3D ASM, rotation, translation, and scale parameters. If the head turns to one side, some points might be occluded but they are still considered for the estimation of the parameters. A robust error norm that reduces (or ideally cancels) the influence of occluded points is applied. With some algebraic transformations the computational cost per frame can be further reduced. The proposed algorithm is evaluated on the basis of the Airplane Behavior Corpus.

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“…The proposed algorithm was evaluated on the basis of the Airplane Behavior Corpus (ABC) that was presented in [15]. The corpus contains a total of 11.5 h video.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Robust tracking of facial feature points with 3D Active Shape Models

Kaiser

Arsic

Sural

et al. 2010

2010 IEEE International Conference on Image Processing

Self Cite

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“…The Airplane Behaviour Corpus (ABC) (cf. [23]) is developed for applications related to public transport surveillance. Certain moods were induced using a predefined script, guiding subjects through a storyline.…”

Section: Data Setsmentioning

confidence: 99%

Comparative Study on Normalisation in Emotion Recognition from Speech

Böck

Egorow

Siegert

et al. 2017

Intelligent Human Computer Interaction

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Abstract. The recognition performance of a classifier is affected by various aspects. A huge influence is given by the input data pre-processing. In the current paper we analysed the relation between different normalisation methods for emotionally coloured speech samples deriving general trends to be considered during data pre-processing. From the best of our knowledge, various normalisation approaches are used in the spoken affect recognition community but so far no multi-corpus comparison was conducted. Therefore, well-known methods from literature were compared in a larger study based on nine benchmark corpora, where within each data set a leave-one-speaker-out validation strategy was applied. As normalisation approaches, we investigated standardisation, range normalisation, and centering. These were tested in two possible options: (1) The normalisation parameters were estimated on the whole data set and (2) we obtained the parameters by using emotionally neutral samples only. For classification Support Vector Machines with linear and polynomial kernels as well as Random Forest were used as representatives of classifiers handling input material in different ways. Besides further recommendations we showed that standardisation leads to a significant improvement of the recognition performance. It is also discussed when and how to apply normalisation methods.

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“…The Airplane Behaviour Corpus (ABC) [9] is developed for the special target application of public transport surveillance. In order to induce a certain mood, a script was used, which lead the subjects through a guided storyline.…”

Section: A Acted Emotionsmentioning

confidence: 99%

Exploring dataset similarities using PCA-based feature selection

Siegert

Böck

Wendemuth

et al. 2015

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-In emotion recognition from speech, several wellestablished corpora are used to date for the development of classification engines. The data is annotated differently, and the community in the field uses a variety of feature extraction schemes. The aim of this paper is to investigate promising features for individual corpora and then compare the results for proposing optimal features across data sets, introducing a new ranking method. Further, this enables us to present a method for automatic identification of groups of corpora with similar characteristics. This answers an urgent question in classifier development, namely whether data from different corpora is similar enough to jointly be used as training material, overcoming shortage of material in matching domains. We compare the results of this method with manual groupings of corpora. We consider the established emotional speech corpora AVIC, ABC, DES, EMO-DB, ENTERFACE, SAL, SMARTKOM, SUSAS and VAM, however our approach is general.

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Audiovisual Behavior Modeling by Combined Feature Spaces

Cited by 64 publications

References 9 publications

Robust tracking of facial feature points with 3D Active Shape Models

Robust tracking of facial feature points with 3D Active Shape Models

Comparative Study on Normalisation in Emotion Recognition from Speech

Exploring dataset similarities using PCA-based feature selection

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