Robust face detection in airports

Jimmy Liu Jiang, Kia Fock Loe, Hong Jiang Zhang

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)503-509
Number of pages7
JournalEurasip Journal on Applied Signal Processing
Issue number4
Publication statusPublished - 1 Apr 2004
Externally publishedYes


  • Divide and conquer
  • Face detection
  • Inlier
  • Outlier
  • S-AdaBoost

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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