## 1 Introduction
[27], which learns a projection from both gallery and generic set and rebuilds a sparse dictionary to perform SSPP FR. For the last category, some researchers synthesize some samples for each individual from the single sample to compensate the limited intra-class variations [31; 32; 33; 34; 35; 36; 37]. [PERSON] and [PERSON] [32] constructed expression subspaces and used them to synthesize new expression images. [PERSON] et al. [36] extracted features from images that are augmented by standard augmentation techniques, such as cropping, translation, and filtering, and then applied sparsity-driven sub-dictionary learning and k-LIMAPS for face identification. To solve the lighting effect, [PERSON] et al. [37] proposed a coupled bilinear model that generates virtual images under various illuminations using a single input image, and learned feature space based on these synthesized images to recognize a face image. [PERSON] et al. [33] proposed an expanding sample method based on traditional approach and used the expanded training samples to fine-tune a well-trained deep convolutional neural network model. 3D face morphable model (3 DMM) is widely applied to face modeling and face image synthesis [38; 35; 39; 40]. [PERSON] et al. [38] fitted 3 DMM to face images via cascaded convolutional neural networks (CNN) and generated new images across large poses, which compose a new augmented database, namely 300W-LP. [PERSON] et al. [39] presented a supervised cascaded collaborative regression (CCR) algorithm that exploits 3 DMM-based synthesized faces for robust 2D facial landmark detection. SSPP-DAN introduced in [35] combines face synthesis and domain-adversarial network. It first generates synthetic images with varying poses using 3 DMM and then eliminates the gap between source domain (synthetic data) and target domain (real data) by domain-adversarial network. [PERSON] et al. [40] explored the use of 3 DMM in generating virtual training samples for pose-invariant CRC-based face classification. The core idea of all the aforementioned methods is to train a model that can extract the identity features of face images, and ensure that the features are discriminative enough to find a suitable classification hyperplane that can accurately divide the features of different individuals. Unfortunately, many external factors, such as pose, facial expression, illumination, resolution, etc., heavily affect the appearance of facial images, and the lack of samples with different external factors leads to insufficient learning of feature space and inaccurate classification hyperplane. Being aware of these problems with existing methods, we propose a method to improve SSPP FR from two perspectives: _Normalization_ and _Diversification_. _Normalization_ is to eliminate the external factors so as to extract robust and invariant features, which are helpful for defining more accurate classification hyperplane. _Diversification_ means to enrich intra-variation through generating additional samples with various external factors. More diverse samples also enable the model to learn more discriminative features for distinguishing different individuals. To achieve this goal, a 3D face modeling module including 3D shape recovery and albedo recovery is presented at first. For the albedo recovery particularly, we make full use of the physical imaging principle and face symmetry to complete the invisible areas caused by self-occlusion while reserving the identity information. Since we represent albedo in the form of UV map, which is theoretically invariant to pose, illumination and expression (PIE) variations, we can alleviate the influence of these external factors. Based on the recovered shape and albedo, additional face images with varying pose, illumination, and expression are generated to increase intra-variation. Finally, we are able to improve the SSPP face recognition accuracy thanks to the enriched intra-variation and invariant features. The remaining parts of this paper are organized as follows.