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ABSTRACT: To recognise just the same human reaction (for example, a strong excitement) in different contexts, customary behaviours in these contexts have to be taken into account; e.g. a happy sport audience may be cheering for long time, while a happy theatrical audience may produce only short bursts of laughter in order to not interrupt the performance. Tailoring recognition algorithms to contexts can be achieved by building either a context-specific or a generic system. The former is individually trained for each context to recognise sets of characteristic responses, whereas the latter-in contrast to the context-specific one-adapts to the context via significantly more lightweight modification of parameters. This paper follows the latter way and proposes a simple modification of a hidden Markov model (HMM) classifier that enables end users to adapt the generic system to a context or a personal perception of an annotator by labelling a fairly small number of data samples of each context. For better adaptability to the limited number of the user's annotations, the proposed semi-supervised HMM classifier employs the maximum posterior marginal, rather than the more conventional maximum a posteriori decision rule. The proposed user- and context-adaptable semi-supervised HMM classifier was tested on recognising excitement of a show audience in three contexts (a concert hall, a circus, and a sport event), differing in how the excitement is expressed. In our experiments the proposed classifier recognised reactions of a non-neutral audience with 10% higher accuracy than the conventional HMM and support vector machine based classifiers
Multimedia Systems . Springer-Verlag. Vol. 18 (2012) No: 3, 231-250 ICT. 06/2012; Vol. 18 (2012)(No: 3):231-250.
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ABSTRACT: The majority of recommender systems require explicit user interaction (ranking of movies and TV programmes and/or their metadata,
such as genres, actors etc), which requires user time and effort. Furthermore, such ranking is often done separately by each
person, while merging these manually acquired individual preferences in multi-user environments remains largely an unsolved
problem. This work presents a method for learning a joint model of a multi-user environment from implicit interactions: programme
choices which family members make together and separately. The proposed method allows to adapt to the practices of each particular
family and to protect family privacy, because the joint family model is learned for each family separately. Furthermore, since
the accuracy of machine learning methods is family-dependent and none of the machine learning methods outperforms others for
all families, a fairly lightweight classifier ensemble selection approach is applied for better adaptation to the specifics
of each family. In tests on the real-life TV viewing histories of 20 families, acquired over 5months, the classifier ensemble
achieved an accuracy comparable with that of systems which require explicit user ratings: an average recall of 57% at an average
precision of 30%, despite only a few programme metadata descriptors being available.
Multimedia Systems 04/2012; 15(3):143-157. · 0.73 Impact Factor
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Elena Vildjiounaite,
Julia Kantorovitch, Vesa Kyllönen,
Ilkka Niskanen,
Mika Hillukkala,
Kimmo Virtanen,
Olli Vuorinen,
Satu-Marja Mäkelä,
Tommi Keränen,
Johannes Peltola,
Jani Mäntyjärvi,
Andrew Tokmakoff
Proceedings of the 2011 International Conference on Intelligent User Interfaces, February 13-16, 2011, Palo Alto, CA, USA; 01/2011
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Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing System, EICS 2011, Pisa, Italy, June 13-16, 2011; 01/2011
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Changing Television Environments, 6th European Conference, EuroITV 2008, Salzburg, Austria, July 3-4, 2008, Proceedings; 01/2008
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ABSTRACT: This works presents a user modelling service for a Smart Home – intelligent context-aware environment, providing personalized
proactive support to its inhabitants. Diversity of Smart Home applications imposes various technical and implementation requirements,
such as the need to model dependency of user preferences on context in a unified and convenient way, both for users and for
application developers. This paper introduces the service architecture and currently implemented functionalities: stereotypes-based
profiles initialisation; a GUI for acquisition of context-dependent and context-independent preferences, which provides an
easy way to create own concepts of context ontology and to map them into already existing concepts; and a method to learn
context-dependent user preferences from interaction history.
08/2007: pages 345-349;
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IEEE Pervasive Computing. 01/2006; 5:82-90.
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Artificial Intelligence and Soft Computing, August 28-30, 2006, Palma de Mallorca, Spain; 01/2006
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Advances in Information Retrieval, 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006, Proceedings; 01/2006
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Pervasive Computing, 4th International Conference, PERVASIVE 2006, Dublin, Ireland, May 7-10, 2006, Proceedings; 01/2006
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ABSTRACT: Proceedings of the 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009). Amsterdam, The Netherlands, 10 - 12 Sept. 2009 This work presents a software framework for real time multimodal affect recognition. The framework supports categorical emotional models and simultaneous classification of emotional states along different dimensions. The framework also allows to incorporate diverse approaches to multimodal fusion, proposed by the current state of the art, as well as to adapt to context-dependency of expressing emotions and to different application requirements. The results of using the framework in audio-video based emotion recognition of an audience of different shows (this is a useful information because emotions of co-located people affect each other) confirm the capability of the framework to provide desired functionalities conveniently and demonstrate that use of contextual information increases recognition accuracy. (21 refs.)
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ABSTRACT: Unobtrusive user authentication is more convenient than explicit interaction and can also increase system security because it can be performed frequently, unlike the current “once explicitly and for a long time” practice. Existing unobtrusive biometrics (e.g., face, voice, gait) do not perform sufficiently well for high-security applications, however, while reliable biometric authentication (e.g., fingerprint or iris) requires explicit user interaction. This work presents experiments with a cascaded multimodal biometric system, which first performs unobtrusive user authentication and requires explicit interaction only when the unobtrusive authentication fails. Experimental results obtained for a database of 150 users show that even with a fairly low performance of unobtrusive modalities (Equal Error Rate above 10%), the cascaded system is capable of satisfying a security requirement of a False Acceptance Rate less than 0.1% with an overall False Rejection Rate of less than 0.2%, while authenticating unobtrusively in 65% of cases.
Image and Vision Computing.
