A universal and robust human skin color model using neural networks

S. L. Phung; D. Chai; A. Bouzerdoum

A universal and robust human skin color model using neural networks

S. L. Phung^*, D. Chai, A. Bouzerdoum

^*Corresponding author for this work

Edith Cowan University

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

68 Citations (Scopus)

Abstract

We propose a new image classification technique that utilizes neural networks to classify skin and non-skin pixels in color images. The aim is to develop a universal and robust model of the human skin color that caters for all human races. The ability to detecting solid skin regions in color images by the model is extremely useful in applications such as face detection and recognition, and human gesture analysis. Experimental results have shown that the neural network classifiers can consistently achieve up to 90% accuracy in skin color detection.

Original language	English
Pages	2844-2849
Number of pages	6
Publication status	Published - 2001
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 15 Jul 2001 → 19 Jul 2001

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'01)
Country/Territory	United States
City	Washington, DC
Period	15/07/01 → 19/07/01

Cite this

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title = "A universal and robust human skin color model using neural networks",

abstract = "We propose a new image classification technique that utilizes neural networks to classify skin and non-skin pixels in color images. The aim is to develop a universal and robust model of the human skin color that caters for all human races. The ability to detecting solid skin regions in color images by the model is extremely useful in applications such as face detection and recognition, and human gesture analysis. Experimental results have shown that the neural network classifiers can consistently achieve up to 90% accuracy in skin color detection.",

author = "Phung, {S. L.} and D. Chai and A. Bouzerdoum",

year = "2001",

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note = "International Joint Conference on Neural Networks (IJCNN'01) ; Conference date: 15-07-2001 Through 19-07-2001",

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AU - Phung, S. L.

AU - Chai, D.

AU - Bouzerdoum, A.

PY - 2001

Y1 - 2001

N2 - We propose a new image classification technique that utilizes neural networks to classify skin and non-skin pixels in color images. The aim is to develop a universal and robust model of the human skin color that caters for all human races. The ability to detecting solid skin regions in color images by the model is extremely useful in applications such as face detection and recognition, and human gesture analysis. Experimental results have shown that the neural network classifiers can consistently achieve up to 90% accuracy in skin color detection.

AB - We propose a new image classification technique that utilizes neural networks to classify skin and non-skin pixels in color images. The aim is to develop a universal and robust model of the human skin color that caters for all human races. The ability to detecting solid skin regions in color images by the model is extremely useful in applications such as face detection and recognition, and human gesture analysis. Experimental results have shown that the neural network classifiers can consistently achieve up to 90% accuracy in skin color detection.

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M3 - Paper

AN - SCOPUS:0034858155

SP - 2844

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T2 - International Joint Conference on Neural Networks (IJCNN'01)

Y2 - 15 July 2001 through 19 July 2001

ER -

A universal and robust human skin color model using neural networks

Abstract

Conference

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