Feature selection method for facial representation using parzen-window density estimation

Heng Fui Liau, D. Isa
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引用次数: 1

Abstract

This paper proposes a feature selection method that aims to select an optimal feature subset to representing facial image from the point of view of minimizing the total error rate (TER) of the system. In this proposed approach, the genuine user score distribution and the imposter score distribution are modeled based on a Parzen-window density estimation to enable the direct estimation of total error rate (TER) as reflected by the area under the curve of the overlapping region of both distributions. Particle swarm optimization (PSO) is employed to search for feature subsets which are extracted from discrete cosine transform or principal component analysis that gives minimum TER and in the meantime to reduce the dimensionality of the feature set thereby reducing processing time.
基于parzen窗密度估计的人脸表征特征选择方法
本文提出了一种特征选择方法,从最小化系统的总错误率(TER)的角度出发,选择一个最优的特征子集来表示人脸图像。在该方法中,真实用户得分分布和冒名用户得分分布基于Parzen-window密度估计建模,从而可以直接估计总错误率(TER),这是由两种分布的重叠区域曲线下的面积反映出来的。采用粒子群算法(Particle swarm optimization, PSO)对离散余弦变换或主成分分析提取的特征子集进行搜索,使特征子集的TER最小,同时降低特征集的维数,从而减少处理时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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