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Gender Classification of Human Faces

MPG-Autoren
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Graf,  ABA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Wichmann,  FA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://link.springer.com/content/pdf/10.1007%2F3-540-36181-2_49.pdf
(Verlagsversion)

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Zitation

Graf, A., & Wichmann, F. (2002). Gender Classification of Human Faces. In H. Bülthoff, C. Wallraven, S.-W. Lee, & T. Poggio (Eds.), Biologically Motivated Computer Vision: Second International Workshop, BMCV 2002 Tübingen, Germany, November 22–24, 2002 (pp. 491-500). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-DE54-9
Zusammenfassung
This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.