Cluster-Classification Bayesian Networks for head pose estimation

Head pose estimation is critical in many applications such as face recognition and human-computer interaction. Various classifiers such as LDA, SVM, or nearest neighbor are widely used for this purpose; however, the recognition rates are limited due to the limited discriminative power of these class...

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Bibliographic Details
Published inProceedings of the 21st International Conference on Pattern Recognition (ICPR2012) pp. 2869 - 2872
Main Authors Kafai, M., Bhanu, B., Le An
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2012
Subjects
Accuracy
Bayesian methods
Estimation
Head
Layout
Support vector machines
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Summary:Head pose estimation is critical in many applications such as face recognition and human-computer interaction. Various classifiers such as LDA, SVM, or nearest neighbor are widely used for this purpose; however, the recognition rates are limited due to the limited discriminative power of these classifiers for discretized pose estimation. In this paper, we propose a head pose estimation method using a Cluster-Classification Bayesian Network (CCBN), specifically designed for classification after clustering. A pose layout is defined where similar poses are assigned to the same block. This increases the discriminative power within the same block when similar yet different poses are present. We achieve the highest recognition accuracy on two public databases (CAS-PEAL and FEI) compared to the state-of-the-art methods.
ISBN:9781467322164
1467322164
ISSN:1051-4651
2831-7475