Abstract book (pdf) - ICPR 2010

In this paper, we propose a new algorithm for Brain-Computer Interface (BCI): Spatially Regularized Common Spatial
Patterns (SRCSP). SRCSP is an extension of the famous CSP algorithm which includes spatial a priori in the learning
process, by adding a regularization term which penalizes spatially non smooth filters. We compared SRCSP and CSP algorithms
on data of 14 subjects from BCI competitions. Results suggested that SRCSP can improve performances, around
10% more in classification accuracy, for subjects with poor CSP performances. They also suggested that SRCSP leads to
more physiologically relevant filters than CSP.
09:00-11:10, Paper ThAT9.16
Comparing Multiple Classifiers for Speech-Based Detection of Self-Confidence – A Pilot Study
Krajewski, Jarek, Univ. of Wuppertal
Batliner, Anton, Univ. of Erlangen-Nuremberg
Kessel, Silke, Univ. of Wuppertal
The aim of this study is to compare several classifiers commonly used within the field of speech emotion recognition
(SER) on the speech based detection of self-confidence. A standard acoustic feature set was computed, resulting in 170
features per one-minute speech sample (e.g. fundamental frequency, intensity, formants, MFCCs). In order to identify
speech correlates of self-confidence, the lectures of 14 female participants were recorded, resulting in 306 one-minute
segments of speech. Five expert raters independently assessed the self-confidence impression. Several classification models
(e.g. Random Forest, Support Vector Machine, Naive Bayes, Multi-Layer Perceptron) and ensemble classifiers (AdaBoost,
Bagging, Stacking) were trained. AdaBoost procedures turned out to achieve best performance, both for single models
(AdaBoost LR: 75.2% class-wise averaged recognition rate) and for average boosting (59.3%) within speaker-independent
settings.
09:00-11:10, Paper ThAT9.17
Hierarchical Human Action Recognition by Normalized-Polar Histogram
Ziaeefard, Maryam, Sahand Univ. of Tech.
Ebrahimnezhad, Hossein, Sahand Univ. of Tech.
This paper proposes a novel human action recognition approach which represents each video sequence by a cumulative
skeletonized images (called CSI) in one action cycle. Normalized-polar histogram corresponding to each CSI is computed.
That is the number of pixels in CSI which is located in the certain distance and angles of the normalized circle. Using hierarchical
classification in two levels, human action is recognized. In first level, course classification is performed with
whole bins of histogram. In the second level, the more similar actions are examined again employing the special bins and
the fine classification is completed. We use linear multi-class SVM as the classifier in two steps. Real human action dataset,
Weizmann, is selected for evaluation. The resulting average recognition rate of the proposed method is 97.6%.
09:00-11:10, Paper ThAT9.18
Automatic 3D Facial Expression Recognition based on a Bayesian Belief Net and a Statistical Facial Feature Model
Zhao, Xi, Ec. Centrale de Lyon
Huang, Di, Ec. Centrale de Lyon
Dellandréa, Emmanuel, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel
approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and
Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter
computing method. By learning global variations in face landmark configuration (morphology) and local ones in terms of
texture and shape around landmarks, morphable Statistic Facial feature Model (SFAM) allows not only to perform an automatic
landmarking but also to compute the belief to feed the BBN. Tested on the public 3D face expression database
BU-3DFE, our automatic approach allows to recognize expressions successfully, reaching an average recognition rate
over 82%.
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