Jose Rafael Tena

A Validated Method for Dense Non-rigid 3D Face Registration

Deformable surface fitting methods have been widely used to establish dense correspondence across different 3D objects of the same class. Dense correspondence is a critical step in constructing morphable face models for face recognition. In this paper a mainstream method for constructing dense correspondences is evaluated on 912 3D face scans from the Face Recognition Grand Challenge FRGC V1 database. A number of modifications to the standard deformable surface approach are introduced to overcome limitations identified in the evaluation. Proposed modifications include multi-resolution fitting, adaptive correspondence search range and enforcing symmetry constraints. The modified deformable surface approach is validated on the 912 FRGC 3D face scans and is shown to overcome limitations of the standard approach which resulted in gross fitting errors. The modified approach halves the rms fitting error with 98% of points within 0.5mm of their true position compared to 67% with the standard approach.

2D face pose normalisation using a 3D morphable model

The ever growing need for improved security, surveillance and identity protection, calls for the creation of evermore reliable and robust face recognition technology that is scalable and can be deployed in all kinds of environments without compromising its effectiveness. In this paper we study the impact that pose correction has on the performance of 2D face recognition. To measure the effect, we use a state of the art 2D recognition algorithm. The pose correction is performed by means of 3D morphable model. Our results on the non frontal XM2VTS database showed that pose correction can improve recognition rates up to 30%.

Robust albedo estimation from face image under unknown illumination

Uncontrolled illumination poses severe problems for face recognition in practical application scenarios. Many techniques to deal with this problem rely on illumination modeling and face relighting. In this paper we propose a robust approach to face albedo estimation in the framework of illumination modeling with Spherical Harmonics. This technique requires only a single face image under arbitrary illumination and assumes the face shape is known. The recovered face albedo facilitates face rendering under new illumination conditions which is useful for both illumination invariant face recognition and computer animation. In the proposed approach, the consequences of the violation of the assumption of validity of the spherical harmonics model are mitigated by minimising a cost function involving robust forms of the error in both the spherical harmonics model and the smoothness constraint. The robust estimation provides significantly better results than the traditional Least Squares Estimation in the experiments on a 3D face database.

Model-Based Coding of 3D Head Sequences

This paper describes a novel technique for model-based coding of 3D head and head-and-shoulders sequences. First, 3D frames are analyzed and registered using a 3D face model, fixed and known also at the decoder side. Then, shape and texture information are compressed in a lossy fashion in order to reduce spatial and temporal redundancy. Results show that efficient compression can be achieved for this type of data.

Compression for 3D face recognition applications

This paper proposes a novel 3D lossy compression algorithm tailored for 3D faces. We analyse the effects of compression on the face verification rate and measure recognition performances on the face recognition grand challenge database. Whilst preserving the spatial resolution enabling reconstruction of surface details, the proposed scheme achieves substantial compression to the extent that personal 3D biometric data could fit on a 2D barcode.

3D Assisted Face Recognition: A Survey

3D face recognition has lately been attracting ever increasing attention. In this chapter we review the full spectrum of 3D face processing technology, from sensing to recognition. The review covers 3D face modelling, 3D to 3D and 3D to 2D registration, 3D based recognition and 3D assisted 2D based recognition. The fusion of 2D and 3D modalities is also addressed. The chapter complements other reviews in the face biometrics area by focusing on the sensor technology, and by detailing the efforts in 3D face modelling and 3D assisted 2D face matching. A detailed evaluation of a typical state-of-the-art 3D face registration algorithm is discussed and conclusions drawn.

Algorithms for 3D-Assisted Face Recognition

We present a review of current methods for 3D face modeling, 3D to 3D and 3D to 2D registration, 3D based recognition, and 3D assisted 2D based recognition. The emphasis is on the 3D registration which plays a crucial role in the recognition chain. An evaluation study of a mainstream state-of-the-art 3D face registration algorithm is carried out and the results discussed

3D assisted 2d face recognition: methodology

We address the problem of pose and illumination invariance in face recognition and propose to use explicit 3D model and variants of existing algorithms for both pose [Fit01, MSCA04] and illumination normalization [ZS04] prior to applying 2D face recognition algorithm. However, contrary to prior work we will use person specific, rather than general 3D face models. The proposed solution is realistic as for many applications the additional cost of acquiring 3D face images during enrolment of the subjects is acceptable. 3D sensing is not required during normal operation of the face recognition system. The proposed methodology achieves illumination invariance by estimating the illumination sources using the 3D face model. By-product of this process is the recovery of the face skin albedo which can be used as a photometrically normalised face image. Standard face recognition techniques can then be applied to such illumination corrected images.