Skin color detection using artificial immune networks

G. Luh
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引用次数: 3

Abstract

Skin detection is the key technology in various image processing applications such as face detection. The aim of skin detection is to determine if a color pixel is a skin or non-skin color. Skin color is often considered to be a useful and discriminating image feature for facial area since it provides computationally effective yet, robust to variation in scale, orientation and partial occlusion. Nevertheless, skin detection is also an extremely challenging task since the skin color is sensitive to various factors such as illumination, ethnicity, individual characteristics and subject appearances. In this paper, an artificial immune network based skin detection scheme in several skin color spaces is proposed. Particle swarm optimization is employed to train/optimize skin/non-skin immune network classifiers. The performance of the method was evaluated employing images derived from the Internet.
基于人工免疫网络的皮肤颜色检测
皮肤检测是人脸检测等各种图像处理应用中的关键技术。皮肤检测的目的是确定颜色像素是皮肤颜色还是非皮肤颜色。肤色通常被认为是一个有用的和区分面部区域的图像特征,因为它提供了计算有效的,并且对尺度,方向和部分遮挡的变化具有鲁棒性。然而,皮肤检测也是一项极具挑战性的任务,因为肤色对光照、种族、个体特征和受试者外表等各种因素都很敏感。本文提出了一种基于人工免疫网络的多肤色空间皮肤检测方案。采用粒子群算法对皮肤/非皮肤免疫网络分类器进行训练/优化。利用来自互联网的图像对该方法的性能进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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