智能环境下基于核相关权重鉴别分析算法的多特征融合人脸识别

Multi-feature fusion face recognition based on KRWDA algorithm under smart environment

针对智能会议环境下基于单模特征的人脸识别的识别率低、鲁棒性差的问题,提出了一种在智能会议室环境下基于核相关权重鉴别分析(KRWDA)算法的融合全局和局部特征的多特征融合人脸识别方法。基于相关权重鉴别分析算法并结合核方法,提出了一种核相关权重鉴别分析算法,有效解决了小样本问题。利用全局特征和局部特征在识别时所描述的内容和作用的互补性在特征层融合两种特征,全局信息和局部信息分别采用离散余弦变换和Ga-bor小波变换提取。在AMIES2016数据库上的仿真实验表明,本文所提出的方法可以有效地提高系统身份识别的正确率。

In view of the situation of low recognition rate and bad robustness of the face recognition based on the single modal features and smart environment, a new multi-feature fusion face recognition method was proposed based on global and local feature fusion and kernel relevance weighted discriminant analysis（KRWDA） algorithm. In order to solve the problem caused by small sample size, a new algorithm called KRWDA was proposed by combination of the relevance weighted discriminant analysis and the kernel trick. The global features and local features were fused in feature layer using the complementarity of the contents and functions described at recognition, and the global features and the local features were extracted by discrete cosine transform and Gabor wavelet transform respectively. The simulation experiments on database AMIES2016 indicated that the proposed method can enhance the accuracy of identity recognition system effectively.