基于非负张量分解的人脸识别算法研究

人脸识别是生物特征识别中一个活跃的研究领域。非负张量分解作为非负矩阵分解的多线性推广,已被成功应用到人脸识别等领域。提出了基于非负张量分解的人脸识别算法。该方法无需将人脸矩阵向量化,从而保持了人脸矩阵的内部结构,即人脸图像的整体结构,使人脸特征提取更精确。 实验结果表明, 与经典的人脸识别算法如PCA和NMF相比,该算法提供了一种更好的脸部表示模式,提高了人脸识别的正确率。

Research of Face Recognition Algorithm Based on Nonnegative Tensor Factorization

Face recognition is an active research area of biometric identification.Nonnegative tensor factorization is the multiple linear extension of nonnegative matrix factorization,which has been successfully applied to face recognition and other fields.A face recognition algorithm based on nonnegative tensor factorization was proposed.This method need not transform a face matrix into a vector,thereby maintaining the internal structure of the human face matrix,thus maintaining the overall structure of the facial images and making the extraction of facial feature more accurate. The experimental results show that,compared with the classical face recognition algorithms such as PCA and NMF,the face recognition algorithm based on nonnegative matrix factorization provides better representation of face patterns and improves the accurate rate of face recognition.