Spontaneous Expression Recognition using Universal Attribute Model

Nazil, P and Roy, Debaditya and C, Krishna Mohan (2018) Spontaneous Expression Recognition using Universal Attribute Model. IEEE Transactions on Image Processing. p. 1. ISSN 1057-7149

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Spontaneous expression recognition refers to recognizing non-posed human expressions. In literature, most of the existing approaches for expression recognition mainly rely on manual annotations by experts, which is both time-consuming and difficult to obtain. Hence, we propose an unsupervised framework for spontaneous expression recognition that preserves discriminative information for the videos of each expression without using annotations. Initially, a large Gaussian mixture model called universal attribute model (UAM) is trained to learn the attributes of various expressions implicitly. Attributes are the movements of various facial muscles that are combined to form a particular facial expression. Then a concatenated mean vector called the super expression-vector (SEV) is formed by using a maximum a posteriori adaptation of the UAM means for each expression clip. This SEV contains attributes from all the expressions resulting in a high dimensional representation. To retain only the attributes of that particular expression clip, the SEV is decomposed using factor analysis to produce a low-dimensional expression-vector. This procedure does not require any class labels and produces expression-vectors that are distinct for each expression irrespective of high inter-actor variability present in spontaneous expressions. On spontaneous expression datasets like BP4D and AFEW, we demonstrate that expression-vector achieves better performance than state-of-the-art techniques. Further, we also show that UAM trained on a constrained dataset can be effectively used to recognize expressions in unconstrained expression videos.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Expression recognition, feature extraction, universal attribute model, map adaptation, factor analysis, Gaussian mixture model
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 26 Jul 2018 03:53
Last Modified: 26 Jul 2018 03:53
URI: http://raiith.iith.ac.in/id/eprint/4313
Publisher URL: http://doi.org/10.1109/TIP.2018.2856373
OA policy: http://www.sherpa.ac.uk/romeo/issn/1057-7149/
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