Linear sparse feature based face detection in gray images

Xiaofeng Lu; Nanning Zheng; Songfeng Zheng

Linear sparse feature based face detection in gray images

Xiaofeng Lu
, Nanning Zheng
, Songfeng Zheng

College of Artificial Intelligence and Robotics

Xi'an Jiaotong University

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.

Original language	English
Pages	889-892
Number of pages	4
State	Published - 2003
Event	Proceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain Duration: 14 Sep 2003 → 17 Sep 2003

Conference

Conference	Proceedings: 2003 International Conference on Image Processing, ICIP-2003
Country/Territory	Spain
City	Barcelona
Period	14/09/03 → 17/09/03

Cite this

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title = "Linear sparse feature based face detection in gray images",

abstract = "A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.",

author = "Xiaofeng Lu and Nanning Zheng and Songfeng Zheng",

year = "2003",

language = "英语",

pages = "889--892",

note = "Proceedings: 2003 International Conference on Image Processing, ICIP-2003 ; Conference date: 14-09-2003 Through 17-09-2003",

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TY - CONF

T1 - Linear sparse feature based face detection in gray images

AU - Lu, Xiaofeng

AU - Zheng, Nanning

AU - Zheng, Songfeng

PY - 2003

Y1 - 2003

N2 - A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.

AB - A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.

UR - https://www.scopus.com/pages/publications/0344309238

M3 - 论文

AN - SCOPUS:0344309238

SP - 889

EP - 892

T2 - Proceedings: 2003 International Conference on Image Processing, ICIP-2003

Y2 - 14 September 2003 through 17 September 2003

ER -

Linear sparse feature based face detection in gray images

Abstract

Conference

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