# Boosting Face Recognition under Drastic Views Using a Pose AutoAugment Manner
## 2 Related Work  
With the rapid development of CNN, face recognition has made a lot of progress in the past ten years. Although the maturity of face recognition leads the increasing success from research to commercial application, there are still some challenges, such as occlusion [18], age [19], pose [20], and attack [21]. In pose-invariant face recognition, there are two streams: one is image normalization, and the other is face augmentation. Both streams mainly focus on two issues: 3D shape fitting and face generating. Since the 1970s, 3D models have been used for image generating [22; 23; 24; 25]. Many studies apply image generating to object detection, object retrieval [26; 27], and viewpoint estimation [28], etc. Recently, image generating methods have been employed for face recognition [24], face alignment [29], and 3D face reconstruction [30].  
The image normalization methods reconstruct the facial image to 3D face and generate the same views from 3D faces for comparison. [PERSON] et al. [31] employed a reconstructed surface to render nine views, then the test image matched these views in a linear subspace. However, the poses of the rendered face are no more than 24\({}^{\circ}\). [PERSON] et al. [32] reconstructed a 3D face by fitting a 2D facial image and generated multi-view virtual faces. A Gabor feature was extracted from both virtual faces and test faces to identify the same person. [PERSON] et al. [33] and [PERSON] et al. [34] also generated multi-view virtual faces from a reconstructed 3D face, further estimating the viewpoint of the test face for comparing the test face with virtual faces under similar view. [PERSON] et al. [35] reconstructed accurate 3D shapes to transform the pose variant of images to a uniform facial space.  
The face augmentation methods are proposed to fit facial images to 3D shapes and generate multi-view facial images to learn pose-invariant features for comparison. Rather than concentrating on accurate 3D reconstruction, [PERSON] et al. rendered the frontal face with a generic 3D face shape [36]. Then 10 generic shapes were employed to render new facial views [9; 13]. Another idea of 3D shape fitting is fitting a facial image to its real 3D shape, which was proposed by [PERSON] et al. [11]. In terms of face generating, [PERSON] et al. [36] only generated a frontal face for each identity. [PERSON] et al. [24; 37] rendered 15 face images from \(-35^{\circ}\) viewpoint to \(+35^{\circ}\) viewpoint in \(5^{\circ}\) steps, including six viewpoints for training and nine viewpoints for testing. [PERSON] et al. also generated five views \((0^{\circ},\pm 45^{\circ},\pm 75^{\circ})\) to train a pose-invariant CNN model [9; 13]. [PERSON] et al. [15] further developed the idea of face generating [9], and generated five views randomly for each identity.  
Both image normalization and face augmentation methods generate limited views for face recognition. However, the sparse sampling of views may reduce the recognition accuracy [16]. [PERSON] et al. [30] generate numerous views, but to design an end-end 3D reconstruction system. We adapt the idea of image generation [30] to our research. Different from [30], we generate numerous views with searched distribution to train a CNN model aimed at the performance improvement of face recognition under drastic poses. Furthermore, our face augmentation method is compatible with any type of 3D face, and we use face scans in this paper, due to its accurate 3D shape and simplicity of texture mapping to 3D shape via camera calibration.  
To our best knowledge, we are the first to generate arbitrary views that are more accurate and realistic for face training and recognition.