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In this article, we analyze how changing the underlying 3D shape of the baseidentity in face images can distort their overall appearance, especially fromthe perspective of deep face recognition. As done in popular training dataaugmentation schemes, we graphically render real and synthetic face images withrandomly chosen or best-fitting 3D face models to generate novel views of thebase identity. We compare deep features generated from these images to assessthe perturbation these renderings introduce into the original identity. Weperform this analysis at various degrees of facial yaw with the base identitiesvarying in gender and ethnicity. Additionally, we investigate if adding someform of context and background pixels in these rendered images, when used astraining data, further improves the downstream performance of a facerecognition model. Our experiments demonstrate the significance of facial shapein accurate face matching and underpin the importance of contextual data fornetwork training.

Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks