Please use this identifier to cite or link to this item:
|Title:||Robust face detection in airports||Authors:||Jiang, J.L.
|Keywords:||Divide and conquer
|Issue Date:||2004||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 14, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.