| Records |
| Author |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
| Title |
Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization |
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Conference Article |
| Year |
2014 |
Publication |
25th British Machine Vision Conference |
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| Abstract |
We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. |
| Address |
Nottingham; UK; September 2014 |
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Conference |
BMVC |
| Notes |
LAMP; CIC; 600.074; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ RWB2014 |
Serial |
2508 |
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| Author |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
| Title |
From emotions to action units with hidden and semi-hidden-task learning |
Type |
Conference Article |
| Year |
2015 |
Publication |
16th IEEE International Conference on Computer Vision |
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3703-3711 |
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Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training. |
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Santiago de Chile; Chile; December 2015 |
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Conference |
ICCV |
| Notes |
LAMP; 600.068; 600.079 |
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no |
| Call Number |
Admin @ si @ RWB2015 |
Serial |
2671 |
| Permanent link to this record |
