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SDA-based discrete head pose estimation

Oost, H.B. (2009) SDA-based discrete head pose estimation.

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Abstract:The estimation of head position and orientation is an important building stone in many applications of Human-computer interaction. This thesis presents two variations of a monocular image head pose estimator based on Subclass Discriminant Analysis (SDA). The use of subclasses enables the application of discriminant analysis to a wider variety of high-dimensional classication problems. The diculty in applying SDA is in determining the optimal division of the data into subclasses. For a selected number of discrete poses, a specialised one-versus-all classier is generated using a boosting procedure applied to feature selection. The one-versus-all classiers are combined into a discrete head pose estimator. This approach is compared to a multi-class approach using the information learned while training the separate one-versus-all classiers. The performance of these two approaches is evaluated on the Pointing'04 dataset and compared to the performance of the more widely used Linear Discriminant Analysis (LDA) approach. The results show that the image features selected using the boosting procedure are similar to those that would be selected using a face mask. The multi-class approach is shown to be preferable over the one-versus-all approach. Additionally, the SDA classiers are shown to have performance characteristics comparable to those of LDA for both approaches.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:05 communication studies
Programme:Interaction Technology MSc (60030)
Link to this item:http://purl.utwente.nl/essays/60756
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