Abstract
In this paper, a novel model for Gabor-based independent radial basis function (IRBF) neural network is proposed and applied to face recognition. In the new model, a bank of Gabor filters is first built to extract Gabor face representations characterized by selected frequency, locality and orientation to cope with various illuminations, facial expression and poses in face recognition. Then principal component analysis (PCA) is adopted to reduce the dimension of the extracted Gabor face representations for every face sample. At last, a new IRBF neural network is built to extract high-order statistical features of extracted Gabor face representations with lower dimension and to classify these extracted high-order statistical features. According to the experiments on the famous CAS-PEAL face database, our proposed approach could outperform ICA with architecture II (ICA2) and kernel PCA (KPCA) with standing testing sets proposed in the current release disk of the CAS-PEAL face database.
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An, G., Ruan, Q. (2006). A Novel Model for Gabor-Based Independent Radial Basis Function Neural Networks and Its Application to Face Recognition. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_24
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DOI: https://doi.org/10.1007/11893257_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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