Three-dimensional face recognition: An Eigensurface approach

We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching

Optimization of Three-dimensional Face Recognition Algorithms in Financial Identity Authentication

Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced

Template Protection For 3D Face Recognition

The human face is one of the most important biometric modalities for automatic authentication. Three-dimensional face recognition exploits facial surface information. In comparison to illumination based 2D face recognition, it has good robustness and high fake resistance, so that it can be used in high security areas. Nevertheless, as in other common biometric systems, potential risks of identity theft, cross matching and exposure of privacy information threaten the security of the authentication system as well as the user\\u27s privacy. As a crucial supplementary of biometrics, the template protection technique can prevent security leakages and protect privacy. In this chapter, we show security leakages in common biometric systems and give a detailed introduction on template protection techniques. Then the latest results of template protection techniques in 3D face recognition systems are presented. The recognition performances as well as the security gains are analyzed

Towards a more secure border control with 3D face recognition

Biometric data have been integrated in all ICAO compliant passports, since the ICAO members started to implement the ePassport standard. The additional use of three-dimensional models promises significant performance enhancements for border control points. By combining the geometry- and texture-channel information of the face, 3D face recognition systems show an improved robustness while processing variations in poses and problematic lighting conditions when taking the photo. This even holds in a hybrid scenario, when a 3D face scan is compared to a 2D reference image. To assess the potential of three-dimensional face recognition, the 3D Face project was initiated. This paper outlines the approach and research results of this project: The objective was not only to increase the recognition rate but also to develop a new, fake resistant capture device. In addition, methods for protection of the biometric template were researched and the second generation of the international standard ISO/IEC 19794-5:2011 was inspired by the project results

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

3D Face Recognition using Significant Point based SULD Descriptor

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition