基于混合Kotz-型分布的多分类人脸识别方法

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-型分布与广义逆厂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。

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

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.