Abstract
We investigate a number of approaches to pose invariant face recognition. Basically, the methods involve three sequential functions for capturing nonlinear manifolds of face view changes: representation, view-transformation and discrimination. We compare a design in which the three stages are optimized separately, with two techniques which establish the overall transformation by a single stage optimization process. In addition we also develop an approach exploiting a generic 3D face model. A look-up table of facial feature correspondence between different views is applied to an input image, yielding a virtual view face. We show experimentally that the four methods developed individually outperform the classical method of Principal Component Analysis(PCA)-Linear Discriminant Analysis(LDA). Further performance gains are achieved by combining the outputs of these face recognition methods using different fusion strategies.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, TK., Kittler, J. (2004). Combining Classifier for Face Identification at Unknown Views with a Single Model Image. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_61
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DOI: https://doi.org/10.1007/978-3-540-27868-9_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
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