计算机工程

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基于混合Kotz-型分布的多分类人脸识别方法

袁少锋,王士同   

  1. (江南大学数字媒体学院,江苏 无锡 214122)
  • 收稿日期:2013-05-27 出版日期:2013-11-15 发布日期:2013-11-13
  • 作者简介:袁少锋(1986-),男,硕士研究生,主研方向:人工智能,模式识别;王士同,教授、博士生导师
  • 基金项目:

    国家自然科学基金资助项目(61272210)

Multi-classification Face Recognition Method Based on Mixed Kotz-type Distribution

YUAN Shao-feng, WANG Shi-tong   

  1. (School of Digital Media, Jiangnan University, Wuxi 214122, China)
  • Received:2013-05-27 Online:2013-11-15 Published:2013-11-13

摘要:

针对实际人脸图像中含有重尾噪声的问题,提出一种基于混合Kotz-型分布的多分类人脸识别方法。利用Kotz-型分布与广义逆Γ分布混合表现出的较厚拖尾特性,结合核方法和概率统计知识,通过调节混合Kotz-型分布中的参数,估计人脸图像中重尾噪声的拖尾情况。分别向ORL人脸库、Yale人脸库、Randface人脸库添加程度不同的重尾噪声,形成新的含有不同程度重尾噪声的人脸库,通过对3个人脸库进行验证,结果表明,该方法能较好地估计人脸图像的拖尾特性,对含有重尾噪声的人脸图像有较高的识别率。

关键词: Kotz-型分布, 广义逆Γ分布, 人脸识别, 核方法, 概率统计, 重尾噪声

Abstract:

Aiming at the problem of the heavy-tailed characteristics in the actual face image, a face recognition method of multi-classification based on mixed Kotz-type distribution is proposed. Mixed Kotz-type distribution and generalized inverse gamma distribution are often used to represent heavy-tailed characteristics. Based on kernel method and probability statistics, this method adjusts the mixed Kotz-type distribution of the parameters to estimate the facial image in the case of heavy-tailed noise tailing. Varying degrees of heavy-tailed noise are added respectively to the ORL face database, Yale face database, Randface(homemade) face database, and a new heavy-tailed noise with varying degrees of face database is formed. Through the verifying of three face database containing different level heavy-tailed noise, results show that the method can estimate the face image trailing feature containing heavy-tailed noise, and has a higher recognition rate.

Key words: Kotz-type distribution, generalized inverse gamma distribution, face recognition, kernel method, probability statistics, heavy-tailed noise

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