Towards developing robust multimodal databases for emotion analysis

Quirós-Ramírez, María Alejandra; Polikovsky, Senya; Kameda, Yoshinari; Onisawa, Takehisa

doi:10.1109/scis-isis.2012.6505247

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…However, this does not permit self-report, nor the ability to elicit pure emotions. In [32], creation of a multimodal spontaneous emotion database is reported where audio-visual signals are captured along with feet pressure signal, thermal image, and body gesture by eliciting emotion by displaying pictures and videos as well as through interview. Recently, DEAP database [33] has been created which includes face videos of 22 participants along with physiological signal recording such as electro-encephalograph (EEG), electromyograph (EMG), electro-oculograph (EOG), blood volume pulse (BVP), skin temperature, and Galvanic Skin Response (GSR).…”

Section: Brief Review Of Existing Expression Databasesmentioning

confidence: 99%

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The Indian Spontaneous Expression Database for Emotion Recognition

Happy¹,

Patnaik

Routray³

et al. 2017

IEEE Trans. Affective Comput.

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Abstract-Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

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Section: Brief Review Of Existing Expression Databasesmentioning

confidence: 99%

“…The strategy of self-report of emotion and the use of hidden camera to record spontaneous expressions are adapted only in very few databases [30], [34]. Therefore, genuine expressions are captured during the experiments unlike the experiments in [17], [5], [24], [32], [33].…”

Section: Brief Review Of Existing Expression Databasesmentioning

confidence: 99%

The Indian Spontaneous Expression Database for Emotion Recognition

Happy¹,

Patnaik

Routray³

et al. 2017

IEEE Trans. Affective Comput.

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“…Current affective computing techniques can be improved in a number of areas. For example, most of the techniques require the use of multiple physiological signals, which necessitates more sensors [34] and intrusiveness [7] to the user. There is evidence that affect classification using a source with low intrusiveness like HRV is feasible [35], yet it is not accurate enough for real-world applications.…”

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confidence: 99%

Dimensional Affect Recognition from HRV: An Approach Based on Supervised SOM and ELM

Bugnon

Calvo

Milone

2020

IEEE Trans. Affective Comput.

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Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The first is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The first is composed of 16 sessions with different participants and was used to evaluate the models in a classification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coefficient. The concordance coefficient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results shows that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states.

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The Development of Stimuli for Emotional Prosody Research: With Contributions from Prof Dr. Jeanette Altarriba, State University of New York, Albany, USA

Bąk

2016

Emotional Prosody Processing for Non-Native English Speakers

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Towards developing robust multimodal databases for emotion analysis

Cited by 5 publications

References 11 publications

The Indian Spontaneous Expression Database for Emotion Recognition

The Indian Spontaneous Expression Database for Emotion Recognition

Dimensional Affect Recognition from HRV: An Approach Based on Supervised SOM and ELM

The Development of Stimuli for Emotional Prosody Research: With Contributions from Prof Dr. Jeanette Altarriba, State University of New York, Albany, USA

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