一种新的多尺度模型GLSA及其人脸识别的应用

考虑图像的近似信息和细节信息，提出了推广的线性尺度自回归(GLSA)多尺度模型。首先利用该模型建立不同尺度图像间的映射关系；其次使用原始图像及其小波分解结果得到模型中的参数以确定这种映射关系；最后根据该映射关系由低分辨率图像估计高分辨率图像。将该模型用于人脸识别处理，通过比较被测试图像的模型参数和训练集图像的模型参数确定被测试图像的类别。实验结果表明，使用GLSA模型估计得到的图像更加接近目标图像，以该模型为基础的人脸识别系统对光照的鲁棒性较强。

Novel Multiscale Model Named GLSA and Its Application to Face Recognition

A novel multiscale model named generalized linear scale autoregressive(GLSA) is proposed,while both approximation and detail information of images are considered.Firstly,GLSA model is established to represent the map between the pixels residing at images of various resolutions;after that,parameters of the model are obtained from original image and its wavelet decomposition results to decide the map;finally,the identified map is used to estimate images at finer resolution from coarser versions.The model is applied to face recognition.Parameters of the model about testing image are compared with that about training images are compared to classify the testing image.Experimental results show that images estimated by GLSA model are more similar to target images than those estimated by LSA model.The GLSA-based face recognition system is robust to illumination.