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Open Access A Multi-Scale Approach to Skin Pixel Detection

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This paper presents an algorithm to detect skin pixels in an image. Each pixel is classified as a skin or non-skin pixel based on features extracted from its neighborhood. The presented algorithm uses a modified likelihood ratio for classification, and uses a multi-scale approach to classify the pixel in question. The algorithm was developed and evaluated using the ColorFERET dataset. The presented algorithm achieved 95.6 % classification accuracy.
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Keywords: MODIFIED LIKELLIHOOD RATIO; MULTI-SCALE APPROACH; SKIN DETECTION

Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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