Multiple classifiers approach for computational efficiency in multi-scale search based face detection

Hanjin Ryu, Seung Soo Chun, Sanghoon Sull

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The multi-scale search based face detection is essential to use a window scanning technique where the window is scanned pixel-by-pixel to search for faces in various positions and scales within an image. Therefore, detection of faces requires high computation cost which prevents from being used in real time applications. In this paper, we present face detection approach by using multiple classifiers for reducing the search space and improving detection accuracy. We design three face classifiers which take different feature representation of local image 1: gradient, texture, and pixel intensity features. The designed three face classifiers are trained by error back propagation algorithm. The computational efficiency is achieved by coarse-to-fine classification approach. A coarse location of a face is first classified by the gradient feature based face classifier where the window is scanned in large moving steps. From the coarse location of a face, the fine classification is performed to identify the local image as a face where the window is finely scanned. In fine classification, the output of each face classifier is combined and then used for a reliable judgment on the existence of face. Experimental results demonstrate that our proposed method can significantly reduce the number of scans compared to the exhaustive full scanning technique and provides the high detection rate.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages483-492
Number of pages10
Volume4221 LNCS - I
Publication statusPublished - 2006 Oct 31
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sep 242006 Sep 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
CountryChina
CityXi'an
Period06/9/2406/9/28

Fingerprint

Multiple Classifiers
Face Detection
Face recognition
Computational efficiency
Computational Efficiency
Classifiers
Face
Pixels
Classifier
Scanning
Backpropagation algorithms
Pixel
Textures
Gradient
Error Propagation
Back-propagation Algorithm
Search Space
Costs
Texture

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Ryu, H., Chun, S. S., & Sull, S. (2006). Multiple classifiers approach for computational efficiency in multi-scale search based face detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4221 LNCS - I, pp. 483-492). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I).

Multiple classifiers approach for computational efficiency in multi-scale search based face detection. / Ryu, Hanjin; Chun, Seung Soo; Sull, Sanghoon.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I 2006. p. 483-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ryu, H, Chun, SS & Sull, S 2006, Multiple classifiers approach for computational efficiency in multi-scale search based face detection. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4221 LNCS - I, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4221 LNCS - I, pp. 483-492, 2nd International Conference on Natural Computation, ICNC 2006, Xi'an, China, 06/9/24.
Ryu H, Chun SS, Sull S. Multiple classifiers approach for computational efficiency in multi-scale search based face detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I. 2006. p. 483-492. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Ryu, Hanjin ; Chun, Seung Soo ; Sull, Sanghoon. / Multiple classifiers approach for computational efficiency in multi-scale search based face detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I 2006. pp. 483-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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