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Human faces manifest distinct structures and characteristics when observed in different scales. Traditional face recognition techniques mainly rely on low-resolution face images, leading to the lost of significant information contained in the microscopic traits. In this paper, we introduce a multilayer framework for high resolution face recognition exploiting features in multiple scales. Each face image is factorized into four layers: global appearance, facial organs, skins, and irregular details. We employ Multilevel PCA followed by Regularized LDA to model global appearance and facial organs. However, the description of skin texture and irregular details, for which conventional vector representation are not suitable, brings forth the need of developing novel representations. To address the issue, Discriminative Multiscale Texton Features and SIFT-Activated Pictorial Structure are proposed to describe skin and subtle details respectively. To effectively combine the information conveyed by all layers, we further design an metric fusion algorithm adaptively placing emphasis onto the highly confident layers. Through systematic experiments, we identify different roles played by the layers and convincingly show that by utilizing their complementarities, our framework achieves remarkable performance improvement.