针对传统谱算法在人脸识别中的局限，该文提出一种基于改进高斯过程隐变量模型(GP-LVM)的多角度人脸识别算法。首先，通过高斯过程(GP)对人脸流形建立概率模型，得到高斯过程隐变量模型(GP-LVM)；其次，分析GP-LVM得到共有信息(shared information)和独有信息(private information)，利用概率最大化与拉格朗日乘子法得到参照矩阵和参照值；最后，实现多角度人脸识别。选取Yale, JAFFE, FERET, CMU-PIE 4类数据集进行对比实验，实验结果表明：该文提出的算法可以有效地识别多角度人脸，针对无角度人脸识别也具有良好的效果。
The traditional spectrum algorithms are limited in face recognition issue. For its characteristics of issue, a novel multi-angle face recognition method based on modified Gaussian Process Latent Variable Mode (GP-LVM) is proposed. Firstly, the probabilistic model of face manifold is established with the Gaussian Process (GP), and the GP-LVM can be gotten. Secondly, the shared information and private information can be gotten by analyzing the GP-LVM. Thereafter, the reference matrices and the reference values are calculated with maximum probability and Lagrange algorithm. Finally, the multi-angle face recognition can be achieved. The four classes of data sets are selected as the experimental data, which consist of Yale, JAFFE, FERET and CMU-PIE. The experiment results show that the proposed method not only has a great effect to recognize multi-angle face, but it can be applied to no angle face recognition.