Universiti Teknologi Malaysia Institutional Repository

Evolutionary feature selections for face detection system

Mohd. Zin, Zalhan and Khalid, Marzuki and Yusof, Rubiyah (2008) Evolutionary feature selections for face detection system. In: Proceedings - International Symposium on Information Technology 2008, ITSim. Institute of Electrical and Electronics Engineers, New York, pp. 1328-1335. ISBN 978-142442328-6

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Official URL: http://dx.doi.org/10.1109/ITSIM.2008.4631734


Various face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GABoost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time.

Item Type:Book Section
Additional Information:ISBN: 978-142442328-6; International Symposium on Information Technology 2008, ITSim; Kuala Lumpur; 26 August 2008 through 29 August 2008
Uncontrolled Keywords:classifiers, concentration (process), diesel engines, evolutionary algorithms, feature extraction, genetic algorithms, information technology, learning systems, object recognition, real time systems, signal detection
Subjects:Q Science > Q Science (General)
Divisions:Computer Science and Information System (Formerly known)
ID Code:12561
Deposited By: Liza Porijo
Deposited On:09 Jun 2011 08:22
Last Modified:02 Oct 2017 08:49

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