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Comparative Analysis of Benchmark Datasets for Face Recognition Algorithms Verification

  • Paweł Forczmański
  • Magdalena Furman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)

Abstract

The paper presents a problem of recognition of facial portraits in the aspect of benchmark database quality. The aim of the work presented here was to analyse the potential of datasets published over the Internet and the predicted applicability of such data for the task of face recognition performance verification. We gathered 41 datasets created and published by various academic and commercial bodies. In the paper we focus on both pure data characteristics, including the number of images, their spatial resolution, quality, content and usability, as well as more high-level properties, e.g. face orientation, expression, background, lighting, and attributes like hats, glasses and beards. We have chosen several datasets on which we performed more detailed experiments related to face recognition. We employed several database preparation algorithms (cross-validation based on different schemes) to make the results as much objective as possible. Here, Principal Component Analysis was employed, as a standard tool for dimensionality reduction. The classification was performed using simple Euclidean metrics. Performed experiments showed a true potential of selected databases.

Keywords

Face Recognition Face Detection Gesture Recognition Independent Component Analysis Benchmark Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Forczmański
    • 1
  • Magdalena Furman
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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