Full body
expressivity analysis in 3D Natural Interaction: a comparative study
George
Caridakis and Kostas Karpouzis
Overview of the Paper
Non-verbal behavior and
communication are very important research topic in the field of psychology and
cognitive science. Measuring expressive characteristics of body motion, posture,
gestures qualitatively are primarily focused on human-human communication. These
methods also can be extended to human-computer interaction. In order to model
expressivity properly, many researchers studied human movement closely and categorized
them mainly in five binary categories. They are slow/fast, restricted/wide,
weak/strong, small/big, unpleasant/pleasant. These expressivity dimensions are
selected as the most complete approach to body expressivity modeling. This paper
presents preliminary research work on defining and extracting full body
expressivity features.
In this paper, the authors
consider five parameters for modeling behavioral expressivity. They are,
1. Overall
activation
2. Spatial
extent
3. Temporal
expressivity
4. Fluidity
5. Power
To formulate the full body
expressivity feature, the authors define the body pose P as
P = [l, r, S, D, F,
J]
Where, l and r are the 3D
coordinates of left and right hand respectively,
S is binary silhouette image,
D is the depth image,
F is the face information, and
J is skeleton joint of left and right
arm.
Using this information, three
different methods for formulating expressivity are presented in this paper.
They methods are named as silhouette method, limbs method and joints method.
From these methods, the expressiveness parameters are modeled.
Evaluation and Validity of the Paper
In this paper, the authors only
present the initial work. But, they try to formalize the framework of
expressivity measure. Some initial phase result is shown here. Initial dataset
was generated by capturing the video of four users by Microsoft Kinect. Sample
images are shown in figure 1.
They S (silhouette) and D (depth)
image result are shown in figure 2.
From the S and D image, the Joint
(J) are calculated and shown in figure 3.
Improvement Scopes
One extension of this paper will
be to implement all the methods describes in this framework to see how it
works. Then it needs to be validated against some other proposed approaches.
Also it needs to be validated by experience movement analyst for considering it
as a valid framework.
Further Reading
One of
the
interesting articles, which are
cited by this paper, is “How to Distinguish Posed from Spontaneous Smiles using
Geometric Features”, by Michel F. Valstar, Hatice Gunes, and Maja Pantic [2] (Digital
Object Identifier: 10.1145/1322192.1322202).
In this paper, the authors used geometric approach for automatic detection of
posed and spontaneous expression. They fused head, shoulder and face
modalities to distinguish between two smiles.
[1] G. Caridakis and K. Karpouzis, “Full body expressivity
analysis in 3D natural interaction: a comparative study,” in Affective Interaction in Natural
Environments workshop, ICMI 2011 International Conference on Multimodal
Interaction. 14-18th November 2011, Alicante, Spain, 2011.
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