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Automatic Pose Correction for Local Feature-Based Face Authentication

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Abstract

In this paper, we present an automatic face authentication system. Accurate segmentation of prominent facial features is accomplished by means of an extension of the Active Shape Model (ASM) approach, the so-called Active Shape Model with Invariant Optimal Features (IOF-ASM). Once the face has been segmented, a pose correction step is applied, so that frontal face images are synthesized. For the generation of these virtual images, we make use of a subset of the shape parameters extracted from a training dataset and Thin Plate Splines texture mapping. Afterwards, sets of local features are computed from these virtual images. The performance of the system is demonstrated on configurations I and II of the XM2VTS database.

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Author information

Authors and Affiliations

  1. Departamento de Teoría de la Señal y Comunicaciones, Universidad de Vigo, Spain

    Daniel González-Jiménez & José Luis Alba-Castro

  2. Departamento de Tecnología, Universidad Pompeu Fabra, Barcelona, Spain

    Federico Sukno & Alejandro Frangi

Authors
  1. Daniel González-Jiménez

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  2. Federico Sukno

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  3. José Luis Alba-Castro

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  4. Alejandro Frangi

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Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco J. Perales

  2. School of Informatics, University of Edinburgh, James Clerk Maxwell Building, The King’s Buildings, Mayfield Road, EH9 3JZ, Edinburgh, UK

    Robert B. Fisher

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© 2006 Springer-Verlag Berlin Heidelberg

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González-Jiménez, D., Sukno, F., Alba-Castro, J.L., Frangi, A. (2006). Automatic Pose Correction for Local Feature-Based Face Authentication. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_37

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