A unified framework for improving the accuracy of all holistic face identification algorithms

被引：4

作者：

Chen, Liang ^{[1
]}

Tokuda, Naoyuki ^{[2
]}

机构：

[1] Univ No British Columbia, Dept Comp Sci, Prince George, BC V2N 4Z9, Canada

[2] SunFlare Co, R&D Ctr, Shinjuku Ku, Tokyo 1600004, Japan

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2010年 / 33卷 / 1-2期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Electoral College; Face identification; Face recognition; Holistic algorithms; Stability; HAMMING NETWORK; FEATURES;

D O I：

10.1007/s10462-009-9139-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Reconstructing the challenging human face identification process as a stability problem, we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms. The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms.

引用

页码：107 / 122

页数：16

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