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Gabor-like Image Filtering for Transient Feature Detection and Global Energy Estimation Applied to Multi-Expression Classification
Hammal, Z., & Massot , C. (2011). Gabor-like image filtering for transient feature detection and global energy estimation applied to multi-expression classification. In Richard, Paul; Braz, José (Ed.), Computer Vision, Imaging and Computer Graphics. Theory and Applications: Communications in Computer and Information Science (CCIS 229 ed., pp. 135--153). Springer.
An automatic system for facial expression recognition should be able
to recognize on-line multiple facial expressions (i.e. “emotional segments”)
without interruption. The current paper proposes a new method for the
automatic segmentation of “emotional segments” and the dynamic recognition
of the corresponding facial expressions in video sequences. First, a new spatial
filtering method based on Log-Normal filters is introduced for the analysis of
the whole face towards the automatic segmentation of the “emotional
segments”. Secondly, a similar filtering-based method is applied to the
automatic and precise segmentation of the transient facial features (such as
nasal root wrinkles and nasolabial furrows) and the estimation of their
orientation. Finally, a dynamic and progressive fusion process of the permanent
and transient facial feature deformations is made inside each “emotional
segment” for a temporal recognition of the corresponding facial expression.
When tested for automatic detection of “emotional segment” in 96 sequences
from the MMI and Hammal-Caplier facial expression databases, the proposed
method achieved an accuracy of 89%. Tested on 1655 images the automatic
detection of transient features achieved a mean precision of 70 % with an error
of 2.5° for the estimation of the corresponding orientation. Finally compared to
the original model for static facial expression classification, the introduction of
transient features and the temporal information increases the precision of the
classification of facial expression by 12% and compare favorably to human
observers’ performances.
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