Face detection based on skin color in image by neural networks

A. Mohamed, Ying Weng, S. Ipson, Jianmin Jiang
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引用次数: 30

Abstract

Face detection is one of the challenging problems in the image processing. A novel face detection system is presented in this paper. The approach relies on skin based color, while features extracted from two dimentional discreate cosine transfer (DCT) and neural networks. which can be used to detect faces by using skin color from DCT coefficient of Cb and Cr feature vectors. This system contains the skin color which is the main feature of faces for detection and then the skin face candidate is examined by using the neural networks, which learns from the feature of faces to classify whether the original image includes a face or not. The processing stage is based on normalization and discreate cosine transfer ( DCT ). Finally the classification based on neural networks approach. The experiments results on upright frontal color face images from the internet show an a excellent detection rate.
基于图像肤色的神经网络人脸检测
人脸检测是图像处理领域的难点之一。本文提出了一种新的人脸检测系统。该方法依赖于基于皮肤的颜色,同时从二维离散余弦转移(DCT)和神经网络中提取特征。利用Cb和Cr特征向量的DCT系数对肤色进行人脸检测。该系统将人脸的主要特征肤色作为检测对象,然后利用神经网络对候选人脸进行检测,神经网络从人脸特征中学习,对原始图像中是否包含人脸进行分类。处理阶段是基于归一化和离散余弦传递(DCT)。最后提出了基于神经网络的分类方法。实验结果表明,该方法对来自网络的正面正面彩色人脸图像具有良好的检测率。
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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