PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Selection for Gender Classification
Zhihong Zhang and Edwin Hancock
In: Pattern Recognition and Image Analysis - 5th Iberian Conference,IbPRIA 2011, June 8-10, 2011, Las Palmas de Gran Canaria, Spain.

Abstract

Most existing feature selection methods focus on ranking features based on an information criterion to select the best K features. However, several authors find that the optimal feature combinations do not give the best classification performance [6],[5]. The reason for this is that although individual features may have limited relevance to a particular class, when taken in combination with other features it can be strongly relevant to the class. In this paper, we derive a new information theoretic criterion that called multidimensional interaction information (MII) to perform feature selection and apply it to gender determination. In contrast to existing feature selection methods, it is sensitive to the relations between feature combinations and can be used to seek third or even higher order dependencies between the relevant features. We apply the method to features delivered by principal geodesic analysis (PGA) and use a variational EM (VBEM) algorithm to learn a Gaussian mixture model for on the selected feature subset for gender determination. We obtain a classification accuracy as high as 95% on 2.5D facial needle-maps, demonstrating the effectiveness of our feature selection methods.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:8560
Deposited By:Edwin Hancock
Deposited On:13 February 2012