Machine Vision and Applications

, Volume 19, Issue 2, pp 85–103 | Cite as

A unified learning framework for object detection and classification using nested cascades of boosted classifiers

  • Rodrigo Verschae
  • Javier Ruiz-del-Solar
  • Mauricio Correa
Original Paper

Abstract

In this paper a unified learning framework for object detection and classification using nested cascades of boosted classifiers is proposed. The most interesting aspect of this framework is the integration of powerful learning capabilities together with effective training procedures, which allows building detection and classification systems with high accuracy, robustness, processing speed, and training speed. The proposed framework allows us to build state of the art face detection, eyes detection, and gender classification systems. The performance of these systems is validated and analyzed using standard face databases (BioID, FERET and CMU-MIT), and a new face database (UCHFACE).

Keywords

Object detection Boosting Nested cascade classifiers Face detection Eyes detection 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Rodrigo Verschae
    • 1
  • Javier Ruiz-del-Solar
    • 1
  • Mauricio Correa
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de ChileSantiagoChile

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