Maja Pantic
Imperial College London
501 Papers
5.6K Citations
Maja Pantic is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Facial expression. The author has an hindex of 84, co-authored 478 publications. Previous affiliations of Maja Pantic include Delft University of Technology & University of Geneva.
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Papers
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
TL;DR: In this paper, the authors discuss human emotion perception from a psychological perspective, examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data.
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Automatic analysis of facial expressions: the state of the art
TL;DR: The capability of the human visual system with respect to these problems is discussed, and it is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
A Multimodal Database for Affect Recognition and Implicit Tagging
TL;DR: Results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported.
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
Christos Sagonas,Georgios Tzimiropoulos,Stefanos Zafeiriou,Maja Pantic +3 more
- 02 Dec 2013
TL;DR: The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization.
Web-based database for facial expression analysis
Maja Pantic,Michel Valstar,R. Rademaker,Ludo Maat +3 more
- 06 Jul 2005
TL;DR: The MMI facial expression database is presented, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various expressions of emotion, single and multiple facial muscle activation.
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