Skip to main content

Robust Face Detection in Airports


Robust face detection in complex airport environment is a challenging task. The complexity in such detection systems stems from the variances in image background, view, illumination, articulation, and facial expression. This paper presents the S-AdaBoost, a new variant of AdaBoost developed for the face detection system for airport operators (FDAO). In face detection application, the contribution of the S-AdaBoost algorithm lies in its use of AdaBoost's distribution weight as a dividing tool to split up the input face space into inlier and outlier face spaces and its use of dedicated classifiers to handle the inliers and outliers in their corresponding spaces. The results of the dedicated classifiers are then nonlinearly combined. Compared with the leading face detection approaches using both the data obtained from the complex airport environment and some popular face database repositories, FDAO's experimental results clearly show its effectiveness in handling real complex environment in airports.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jimmy Liu Jiang.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jiang, J.L., Loe, KF. & Zhang, H.J. Robust Face Detection in Airports. EURASIP J. Adv. Signal Process. 2004, 837265 (2004).

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI:

Keywords and phrases

  • S-AdaBoost
  • face detection
  • divide and conquer
  • inlier
  • outlier