Abstract
This paper presents a new scheme to initialize and re-estimate Embedded Hidden Markov Models(E-HMM) parameters for face recognition. Firstly, the current samples were assumed to be a subset of the whole training samples, after the training process, the E-HMM parameters and the necessary temporary parameters in the parameter re-estimating process were saved for the possible retraining use. When new training samples were added to the training samples, the saved E-HMM parameters were chosen as the initial model parameter. Then the E-HMM was retrained based on the new samples and the new temporary parameters were obtained. Finally, these temporary parameters were combined with saved temporary parameters to form the final E-HMM parameters for representing one person face. Experiments on ORL databases show the improved method is effective.
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Xue, B., Xue, W., Jiang, Z. (2005). Improved Parameters Estimating Scheme for E-HMM with Application to Face Recognition. In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics. ICB 2006. Lecture Notes in Computer Science, vol 3832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11608288_27
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DOI: https://doi.org/10.1007/11608288_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31111-9
Online ISBN: 978-3-540-31621-3
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