3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Developing a Computer System for the Generation of Unique Wrinkle Maps for Human Faces. Generating 2D Wrinkle Maps using Various Image Processing Techniques and the Design of 3D Facial Ageing System using 3D Modelling Tools.

Facial Ageing (FA) is a very fundamental issue, as ageing in general, is part of our daily life process. FA is used in security, finding missing children and other applications. It is also a form of Facial Recognition (FR) that helps identifying suspects. FA affects several parts of the human face under the influence of different biological and environmental factors. One of the major facial feature changes that occur as a result of ageing is the appearance and development of wrinkles. Facial wrinkles are skin folds; their shapes and numbers differ from one person to another, therefore, an advantage can be taken over these characteristics if a system is implemented to extract the facial wrinkles in a form of maps. This thesis is presenting a new technique for three-dimensional facial wrinkle pattern information that can also be utilised for biometric applications, which will back up the system for further increase of security. The procedural approaches adopted for investigating this new technique are the extraction of two-dimensional wrinkle maps of frontal human faces for digital images and the design of three-dimensional wrinkle pattern formation system that utilises the generated wrinkle maps. The first approach is carried out using image processing tools so that for any given individual, two wrinkle maps are produced; the first map is in a binary form that shows the positions of the wrinkles on the face while the other map is a coloured version that indicates the different intensities of the wrinkles. The second approach of the 3D system development involves the alignment of the binary wrinkle maps on the corresponding 3D face models, followed by the projection of 3D curves in order to acquire 3D representations of the wrinkles. With the aid of the coloured wrinkle maps as well as some ageing parameters, simulations and predictions for the 3D wrinkles are performed

3-D Face Analysis and Identification Based on Statistical Shape Modelling

This paper presents an effective method of statistical shape representation for automatic face analysis and identification in 3-D. The method combines statistical shape modelling techniques and the non-rigid deformation matching scheme. This work is distinguished by three key contributions. The first is the introduction of a new 3-D shape registration method using hierarchical landmark detection and multilevel B-spline warping technique, which allows accurate dense correspondence search for statistical model construction. The second is the shape representation approach, based on Laplacian Eigenmap, which provides a nonlinear submanifold that links underlying structure of facial data. The third contribution is a hybrid method for matching the statistical model and test dataset which controls the levels of the model’s deformation at different matching stages and so increases chance of the successful matching. The proposed method is tested on the public database, BU-3DFE. Results indicate that it can achieve extremely high verification rates in a series of tests, thus providing real-world practicality

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page