Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 44, Issue 12, Pages 2443-2457Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2308091
Keywords
4-D data; expression recognition; Hidden Markov model (HMM); random forest; Riemannian geometry; temporal analysis
Categories
Funding
- ANR through the 3-D Face Analyzer project [ANR-10-INTB-0301]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1208959] Funding Source: National Science Foundation
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In this paper, we present an automatic approach for facial expression recognition from 3-D video sequences. In the proposed solution, the 3-D faces are represented by collections of radial curves and a Riemannian shape analysis is applied to effectively quantify the deformations induced by the facial expressions in a given subsequence of 3-D frames. This is obtained from the dense scalar field, which denotes the shooting directions of the geodesic paths constructed between pairs of corresponding radial curves of two faces. As the resulting dense scalar fields show a high dimensionality, Linear Discriminant Analysis (LDA) transformation is applied to the dense feature space. Two methods are then used for classification: 1) 3-D motion extraction with temporal Hidden Markov model (HMM) and 2) mean deformation capturing with random forest. While a dynamic HMM on the features is trained in the first approach, the second one computes mean deformations under a window and applies multiclass random forest. Both of the proposed classification schemes on the scalar fields showed comparable results and outperformed earlier studies on facial expression recognition from 3-D video sequences.
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