Abstract book (pdf) - ICPR 2010

(ii) multiple kernel Support Vector Machine based human action recognition. Experimental results on a set of test database
show that our proposed method is very efficient and effective to recognize human actions using few parameters, independent
of dimensions, shadows, and viewpoints.
09:00-11:10, Paper ThAT9.23
Action Recognition by Multiple Features and Hyper-Spheremulti-Class SVM
Liu, Jia, Shanghai Jiao Tong Univ.
Yang, Jie, Shanghai Jiao Tong Univ.
Zhang, Yi, Shanghai Jiao Tong Univ.
He, Xiangjian, University of Technology, Sydney
In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition
in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation
is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age,
gender etc.). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids
and 2-D SIFT), and ii) the higher-order statistical models of interest points, which aims to capture the global information
of the actor. We construct video representation in terms of local space-time features and global features and integrate such
representations with hyper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed
approach is effective. An additional experiment shows that using both local and global features provides a richer representation
of human action when compared to the use of a single feature type.
09:00-11:10, Paper ThAT9.24
Multimodal Recognition of Cognitive Workload for Multitasking in the Car
Putze, Felix, Karlsruhe Inst. of Tech.
Jarvis, Jan-Philip, Karlsruhe Inst. of Tech.
Schultz, Tanja, Univ. Karlsruhe
This work describes the development and evaluation of a recognizer for different levels of cognitive workload in the car.
We collected multiple biosignal streams (skin conductance, pulse, respiration, EEG) during an experiment in a driving
simulator in which the drivers performed a primary driving task and several secondary tasks of varying difficulty. From
this data, an SVM based workload classifier was trained and evaluated, yielding recognition rates of up to for three levels
of workload.
09:00-11:10, Paper ThAT9.25
Automatic Facial Action Detection using Histogram Variation between Emotional States
Senechal, Thibaud, ISIR, UPMC
Bailly, Kevin, Univ. PIERRE 1 MARIE CURIE - PARIS 6
Prevost, Lionel, Univ. PIERRE 1 MARIE CURIE - PARIS 6
This article presents an appearance based method to detect automatically facial actions. Our approach focuses on reducing
features sensitivity to identity of the subject. We compute from an expressive image a Local Gabor Binary Pattern (LGBP)
histogram and synthesize a LGBP histogram approaching the one we would compute on a neutral face. Difference between
these two histograms are used as inputs of Support Vector Machine (SVM) binary detectors associated with a new kernel:
the Histogram Difference Intersection (HDI) kernel. Experimental results carried out for 16 Action Units (AUs) on the
benchmark Cohn-Kanade database can be compared favorably with two state-of-the-art methods.
09:00-11:10, Paper ThAT9.27
Decoding Finger Flexion from Electrocorticographic Signals using Sparse Gaussian Process
Wang, Zuoguan, RPI
Ji, Qiang, RPI
Schalk, Gerwin, NYS Dept of Health
Miller, Kai J., Univ. of Washington,
A brain-computer interface (BCI) creates a direct communication pathway between the brain and an external device, and
can thereby restore function in people with severe motor disabilities. A core component in a BCI system is the decoding
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