3D face reconstruction from mugshots: Application to arbitrary view face recognition

Mugshots while routinely acquired by law enforcement agencies are under utilized by automated face recognition systems. In this paper, we propose a regression based approach to reconstruct textured full 3D face models from multi-view mugshot images. Using landmarks from the input frontal and profile mugshots of a subject, our method reconstructs his ⧹ her 3D face shape via either linear or nonlinear regressors. The texture of the mugshot images is mapped to the reconstructed 3D face shape via an efficient seamless texture recovery scheme. Compared with existing 3D face reconstruction methods, the proposed method more effectively utilizes the three-view mugshot face images collected during booking. The reconstructed 3D faces are used to generate realistic multi-view face images to enlarge the gallery and facilitate arbitrary view face recognition. Evaluation experiments have been done on BFM and Bosphorus databases in terms of reconstruction accuracy, and on Multi-PIE and Color FERET databases in terms of recognition accuracy. The results show that the proposed method can reduce the 3D face reconstruction error of the best competitive method from 2.31 mm to 1.88 mm, and improve the recognition accuracy of state-of-the-art deep learning based face matchers by as much as ~4% on Multi-PIE and ~2% on Color FERET despite the high baseline set by them.