Face detection using classifiers cascade based on vector angle measure and multi-modal representation

F. Flitti; A. Bermak

doi:10.1109/SIPS.2007.4387605

Face detection using classifiers cascade based on vector angle measure and multi-modal representation

F. Flitti^*, A. Bermak

^*Corresponding author for this work

Hong Kong University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory.

Original language	English
Title of host publication	2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings
Pages	539-542
Number of pages	4
DOIs	https://doi.org/10.1109/SIPS.2007.4387605
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE Workshop on Signal Processing Systems, SiPS 2007 - Shanghai, China Duration: 17 Oct 2007 → 19 Oct 2007

Publication series

Name	IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)	1520-6130

Conference

Conference	2007 IEEE Workshop on Signal Processing Systems, SiPS 2007
Country/Territory	China
City	Shanghai
Period	17/10/07 → 19/10/07

Keywords

Classifiers cascade
Face detection
Low confidence decision based training
Vector angle

Access to Document

10.1109/SIPS.2007.4387605

Cite this

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title = "Face detection using classifiers cascade based on vector angle measure and multi-modal representation",

abstract = "This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory.",

keywords = "Classifiers cascade, Face detection, Low confidence decision based training, Vector angle",

author = "F. Flitti and A. Bermak",

year = "2007",

doi = "10.1109/SIPS.2007.4387605",

language = "English",

isbn = "1424412226",

series = "IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation",

pages = "539--542",

booktitle = "2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings",

note = "2007 IEEE Workshop on Signal Processing Systems, SiPS 2007 ; Conference date: 17-10-2007 Through 19-10-2007",

}

Flitti, F & Bermak, A 2007, Face detection using classifiers cascade based on vector angle measure and multi-modal representation. in 2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings., 4387605, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, pp. 539-542, 2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Shanghai, China, 17/10/07. https://doi.org/10.1109/SIPS.2007.4387605

Face detection using classifiers cascade based on vector angle measure and multi-modal representation. / Flitti, F.; Bermak, A.
2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings. 2007. p. 539-542 4387605 (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Bermak, A.

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Y1 - 2007

N2 - This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory.

AB - This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory.

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KW - Low confidence decision based training

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Face detection using classifiers cascade based on vector angle measure and multi-modal representation

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Keywords

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