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Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
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Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
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2010 |
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EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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2010 |
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9 |
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800 |
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MV |
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fernando @ fernando @ |
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2423 |
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Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
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Title |
Automatic Detection of Dominance and Expected Interest |
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Journal Article |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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12 |
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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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1110-8657 |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPR2010d |
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1283 |
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Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
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Title |
Deteccion automatica de la dominancia en conversaciones diadicas |
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Journal Article |
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Year |
2010 |
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Escritos de Psicologia |
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EP |
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3 |
Issue |
2 |
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41–45 |
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Dominance detection; Non-verbal communication; Visual features |
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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. |
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1989-3809 |
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HUPBA; OR; MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ EMV2010 |
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1315 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
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Title |
Boosted Online Learning for Face Recognition |
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Journal Article |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
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2 |
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530–538 |
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Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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1083–4419 |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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Journal Article |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
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39 |
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4 |
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935–944 |
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Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2009a |
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1218 |
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