[1]崔 鹏,燕天天. 融合 YCbCr 肤色模型与改进的 Adaboost 算法的人脸检测[J].哈尔滨理工大学学报,2018,(02):91-96.[doi:10. 15938/j. jhust. 2018. 02. 016]
 CUI Peng,YAN Tian-tian. Face Detection Combining the YCbCr Skin Color Model with Improved Adaboost Algorithm[J].哈尔滨理工大学学报,2018,(02):91-96.[doi:10. 15938/j. jhust. 2018. 02. 016]
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 融合 YCbCr 肤色模型与改进的 Adaboost 算法的人脸检测
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2018年02期
页码:
91-96
栏目:
材料科学与工程
出版日期:
2018-04-25

文章信息/Info

Title:
 Face Detection Combining the YCbCr Skin Color Model with Improved Adaboost Algorithm
文章编号:
1007-2683( 2018) 02-0091-06
作者:
 崔 鹏 燕天天
 ( 哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080)
Author(s):
 CUI PengYAN Tian-tian
 ( School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)
关键词:
 :人脸检测肤色检测 YCbCr 颜色空间 Adaboost 算法权重
Keywords:
 face detection skin color detection YCbCr color space Adaboost algorithm weight
分类号:
TP391. 4
DOI:
10. 15938/j. jhust. 2018. 02. 016
文献标志码:
A
摘要:
 提出了一种将人脸肤色检测与改进的 Adaboost 算法相结合的人脸检测方法。将人脸 图像从 RGB 颜色空间映射到 YCbCr 颜色空间,建立肤色模型进行人脸相似度求取,通过形态学处 理得到候选人脸区域。在训练阶段,通过调整加权误差分布限制目标类权重的扩张,通过修改目标 权重更新抑制训练退化和训练目标类权重分布过适应现象。用改进的 Adaboost 算法对得到的人 脸候选区域进行检测,提高了检测速度。实验结果表明,该算法抑制了训练目标类权重过适应现 象,有效的提高了检测率和检测速度
Abstract:
 This paper proposed a new face detection method which based on the Adaboost and the face skin color detection. Firstly,face images are mapped from RGB color space to the YCbCr color space,and thenthe skin color model is established to obtain the similarity of the face,the face region of the candidate isobtained by the morphological processing. In the training stage,the expansion of the target weight is restrictedby adjusting the weight of the error distribution. The phenomenon,training degradation and over adapt the distribution of the training target weight,is suppressed by modifying the renewal of the weight. The improved Adaboost algorithm is used to detect the candidate region of the face,which can improve the detection speed. The experimental results show that the proposed algorithm can effectively improve the detection rate and the detection speed,which can restrain the over adaptation of the training object.

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更新日期/Last Update: 2018-06-30