Please use this identifier to cite or link to this item: https://doi.org/10.1155/S1110865704310206
Title: Robust face detection in airports
Authors: Jiang, J.L. 
Loe, K.-F. 
Zhang, H.J.
Keywords: Divide and conquer
Face detection
Inlier
Outlier
S-AdaBoost
Issue Date: 2004
Source: Jiang, J.L.,Loe, K.-F.,Zhang, H.J. (2004). Robust face detection in airports. Eurasip Journal on Applied Signal Processing 2004 (4) : 503-509. ScholarBank@NUS Repository. https://doi.org/10.1155/S1110865704310206
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-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.
Source Title: Eurasip Journal on Applied Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/39722
ISSN: 11108657
DOI: 10.1155/S1110865704310206
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