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Sergio Escalera, R. M. Martinez, Jordi Vitria, Petia Radeva, & Maria Teresa Anguera. (2010). Deteccion automatica de la dominancia en conversaciones diadicas. EP - Escritos de Psicologia, 3(2), 41–45.
Abstract: Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.
Keywords: Dominance detection; Non-verbal communication; Visual features
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Sergio Escalera, Petia Radeva, & Oriol Pujol. (2007). Complex Salient Regions for Computer Vision Problems. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on.
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Sergio Escalera, Petia Radeva, Jordi Vitria, Xavier Baro, & Bogdan Raducanu. (2010). Modelling and Analyzing Multimodal Dyadic Interactions Using Social Networks. In 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction..
Abstract: Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from
multimodal dyadic interactions. First, speech detection is performed through an audio/visual fusion scheme based on stacked sequential learning. In the audio domain, speech is detected through clusterization of audio features. Clusters
are modelled by means of an One-state Hidden Markov Model containing a diagonal covariance Gaussian Mixture Model. In the visual domain, speech detection is performed through differential-based feature extraction from the segmented
mouth region, and a dynamic programming matching procedure. Second, in order to model the dyadic interactions, we employed the Influence Model whose states
encode the previous integrated audio/visual data. Third, the social network is extracted based on the estimated influences. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The results
are reported both in terms of accuracy of the audio/visual data fusion and centrality measures used to characterize the social network.
Keywords: Social interaction; Multimodal fusion, Influence model; Social network analysis
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Sergio Escalera, & Petia Radeva. (2004). Fast greyscale road sign model matching and recognition.
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Sergio Escalera, Oriol Pujol, Petia Radeva, Jordi Vitria, & Maria Teresa Anguera. (2010). Automatic Detection of Dominance and Expected Interest. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 12.
Abstract: Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
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Sergio Escalera, Oriol Pujol, Petia Radeva, & Jordi Vitria. (2009). Measuring Interest of Human Dyadic Interactions. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 45–54).
Abstract: In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2006). Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a novel framework to detect and classify objects in cluttered scenes.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2006). ECOC-ONE: A novel coding and decoding strategy.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2006). Decoding of Ternary Error Correcting Output Codes. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 753–763.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Robust Complex Salient Regions. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:113–121.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Traffic Sign Classification using Error Correcting Techniques. In 2nd International Conference on Computer Vision Theory and Applications (281–285).
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Detection of Complex Salient Regions. EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Sub-Class Error-Correcting Output Codes. In Computer Vision Systems. 6th International Conference (Vol. 5008, 494–504).
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Loss-Weighted Decoding for Error-Correcting Output Coding. In 3rd International Conference on Computer Vision Theory and Applications, (Vol. 2, 117–122).
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