Superimposition-guided Facial Reconstruction from Skull

We develop a new algorithm to perform facial reconstruction from a given skull. This technique has forensic application in helping the identification of skeletal remains when other information is unavailable. Unlike most existing strategies that directly reconstruct the face from the skull, we utilize a database of portrait photos to create many face candidates, then perform a superimposition to get a well matched face, and then revise it according to the superimposition. To support this pipeline, we build an effective autoencoder for image-based facial reconstruction, and a generative model for constrained face inpainting. Our experiments have demonstrated that the proposed pipeline is stable and accurate.

[1]  Caroline Wilkinson,et al.  Facial reconstruction – anatomical art or artistic anatomy? , 2010, Journal of anatomy.

[2]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[3]  Michael J. Black,et al.  Learning a model of facial shape and expression from 4D scans , 2017, ACM Trans. Graph..

[4]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  R. Bruegge,et al.  The Magna Database: A Database of Three-Dimensional Facial Images for Research in Human Identification and Recognition , 2008 .

[7]  Sami Romdhani,et al.  A 3D Face Model for Pose and Illumination Invariant Face Recognition , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[8]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[9]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[10]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Flavio Prieto,et al.  3D Curvature-Based Shape Descriptors for Face Segmentation: An Anatomical-Based Analysis , 2010, ISVC.

[12]  Francesco Santini,et al.  Face Reconstruction from Skull Shapes and Physical Attributes , 2009, DAGM-Symposium.

[13]  Patrick Pérez,et al.  MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[15]  Li Xu,et al.  Shepard Convolutional Neural Networks , 2015, NIPS.

[16]  Jiwen Lu,et al.  Single Sample Face Recognition via Learning Deep Supervised Autoencoders , 2015, IEEE Transactions on Information Forensics and Security.

[17]  Qiang Ji,et al.  Discriminative Deep Face Shape Model for Facial Point Detection , 2015, International Journal of Computer Vision.

[18]  Ming-Hsuan Yang,et al.  Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network , 2016, ECCV.

[19]  Sebastian Nowozin,et al.  Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.

[20]  Oscar Cordón,et al.  Forensic identification by computer-aided craniofacial superimposition: A survey , 2011, CSUR.

[21]  Arman Savran,et al.  Bosphorus Database for 3D Face Analysis , 2008, BIOID.

[22]  Hans-Peter Seidel,et al.  Reanimating the dead: reconstruction of expressive faces from skull data , 2003, ACM Trans. Graph..

[23]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

[24]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[25]  K. Taylor.  Forensic Art and Illustration , 2000 .

[26]  Stefanos Zafeiriou,et al.  The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[27]  Paul Suetens,et al.  Craniofacial reconstruction using a combined statistical model of face shape and soft tissue depths: methodology and validation. , 2006, Forensic science international.

[28]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[29]  Carlos D. Castillo,et al.  An All-In-One Convolutional Neural Network for Face Analysis , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[30]  Alan C. Bovik,et al.  Anthropometric 3D Face Recognition , 2010, International Journal of Computer Vision.

[31]  Syed Zulqarnain Gilani,et al.  Dense 3D Face Correspondence , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Shuicheng Yan,et al.  Robust LSTM-Autoencoders for Face De-Occlusion in the Wild , 2016, IEEE Transactions on Image Processing.

[33]  J. Muller,et al.  A template-deformation method for facial reproduction , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[34]  V M Phillips,et al.  Facial reconstruction: utilization of computerized tomography to measure facial tissue thickness in a mixed racial population. , 1996, Forensic science international.

[35]  A. Falsetti,et al.  Forensic anthropology , 2013 .

[36]  Simon Lucey,et al.  Deformable Model Fitting by Regularized Landmark Mean-Shift , 2010, International Journal of Computer Vision.

[37]  Enhong Chen,et al.  Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.

[38]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[39]  Jianfei Cai,et al.  CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  David Meger,et al.  Improved Adversarial Systems for 3D Object Generation and Reconstruction , 2017, CoRL.

[41]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[42]  Mark W. Jones,et al.  Facial Reconstruction Using Volumetric Data , 2001, VMV.

[43]  Heng Yang,et al.  Facial feature point detection: A comprehensive survey , 2014, Neurocomputing.

[44]  Yiying Tong,et al.  FaceWarehouse: A 3D Facial Expression Database for Visual Computing , 2014, IEEE Transactions on Visualization and Computer Graphics.

[45]  Minh N. Do,et al.  Semantic Image Inpainting with Deep Generative Models , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).