An Enhanced Feature Selection Tool for Face Detection using Genetic Algorithm
K. Mohan1, K. Ramanaiah2, S. A. K. Jilani3

1K. Mohan, Assistant Professor, Department of Electronics and Communication Engineering, SEAT, Tirupati, (Andhra Pradesh), India.
2Dr. K. V. Ramanaiah, Professor, Department of Electronics and Ccommunication Engineering, Yogi Vemana University, Kadapa, Kadapa dist., (Andhra Pradesh), India.
3Dr. S.A.K. Jilani, Professor and Director, Research and Development cell, Department of Electronics and Communication Enginerering in MITS, Madanapalee, (Andhra Pradesh) ,india.
Manuscript received on 21 May 2013 | Revised Manuscript received on 28 May 2013 | Manuscript published on 30 May 2013 | PP: 6-11 | Volume-2 Issue-2, May 2013 | Retrieval Number: B0548052213/2013©BEIESP
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Abstract: 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, GA Boost 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 but gives higher detection rates.
Keywords: Genetic Algorithm, Cascade of Classifiers, Adaboost, rectangle features.

Scope of the Article: Classifiers