Robust face detection in airports

Authors:
Jimmy Liu Jiang;Kia-Fock Loe;Hong Jiang Zhang
Affiliations:
School of Computing, National University of Singapore, Singapore;School of Computing, National University of Singapore, Singapore;Microsoft Research Asia, Beijing Sigma Center, Beijing, China
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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Abstract

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-Ada Boost, a new variant of AdaBoost developed for the face detection system for airport operators (FDAO). In face detection application, the contribution of the S-Ada Boost 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.