3D Face Matching Research Articles

3D face recognition using covariance based descriptors

In this paper, we propose a new 3D face recognition method based on covariance descriptors. Unlike feature-based vectors, covariance-based descriptors enable the fusion and the encoding of different types of features and modalities into a compact representation. The covariance descriptors are symmetric positive definite matrices which can be viewed as an inner product on the tangent space of (Symd+) the manifold of Symmetric Positive Definite (SPD) matrices. In this article, we study geodesic distances on the Symd+ manifold and use them as metrics for 3D face matching and recognition. We evaluate the performance of the proposed method on the FRGCv2 and the GAVAB databases and demonstrate its superiority compared to other state of the art methods.

A Mobile Structured Light System for 3D Face Acquisition

A mobile sensor based on fringe projection techniques is developed with the goal of acquiring face 3D and color with a smartphone device. The system consists of a portable pico-projector and an Android-based smartphone. The data acquisition, pattern generation. and reconstruction of the final 3D point cloud are all driven by the smartphone. We present results on the root-mean-square error (RMSE) of the sensor and on 3D face matching.

Local binary patterns on triangular meshes: Concept and applications

In this paper, we introduce an original framework for computing local binary like-patterns on 2D mesh manifolds (i.e., surfaces in the 3D space). This framework, dubbed mesh-LBP, preservers the simplicity and the adaptability of the 2D LBP and has the capacity of handling both open and close mesh surfaces without requiring normalization as compared to its 2D counterpart. We describe the foundations and the construction of mesh-LBP and showcase the different LBP patterns that can be generated on the mesh. In the experimentation, we provide evidence of the uniform patterns in the mesh-LBP, the repeatability of its descriptors, and its robustness to moderate shape deformations. Then, we show how the mesh-LBP descriptors can be adapted to a number of surface local and global analysis including 3D texture classification and retrieval, and 3D face matching. We also compare the performance of the mesh-LBP descriptors with a bunch of state of the art surface descriptors.

얼굴 포즈 추정을 이용한 다중 RGB-D 카메라 기반의 2D - 3D 얼굴 인증을 위한 시스템

Face recognition is a big challenge in surveillance system since different rotation angles of the face make the difficulty to recognize the face of the same person. This paper proposes a novel method to recognize face with different head poses by using 3D information of the face. Firstly, head pose estimation (estimation of different head pose angles) is accomplished by the POSIT algorithm. Then, 3D face image data is constructed by using head pose estimation. After that, 2D image and the constructed 3D face matching is performed. Face verification is accomplished by using commercial face recognition SDK. Performance evaluation of the proposed method indicates that the error range of head pose estimation is below 10 degree and the matching rate is about 95%.

3D Face Matching Based on Depth-Level Curves

In the domain of 3D face matching, many techniques have been developed as variants of 3D facial matching approaches that reduce the amount of facial data into few 3D curves. In the literature, many curves have been considered: level-curves, radial curves, iso-stripes, crest lines, etc... In this paper, we exploit curve concept to represent 3D facial geometric. First, depth-level curves were extracted to present 3D facial data then, we investigate the dimensionality reduction offered by Random Projection to perform an artificial system for face recognition using Back-propagation neural network. Experiment was conducted using vrml files from FRAV Database considering only one training sample per person.

3D Face Recognition Using Isogeodesic Stripes

In this paper, we present a novel approach to 3D face matching that shows high effectiveness in distinguishing facial differences between distinct individuals from differences induced by nonneutral expressions within the same individual. The approach takes into account geometrical information of the 3D face and encodes the relevant information into a compact representation in the form of a graph. Nodes of the graph represent equal width isogeodesic facial stripes. Arcs between pairs of nodes are labeled with descriptors, referred to as 3D Weighted Walkthroughs (3DWWs), that capture the mutual relative spatial displacement between all the pairs of points of the corresponding stripes. Face partitioning into isogeodesic stripes and 3DWWs together provide an approximate representation of local morphology of faces that exhibits smooth variations for changes induced by facial expressions. The graph-based representation permits very efficient matching for face recognition and is also suited to being employed for face identification in very large data sets with the support of appropriate index structures. The method obtained the best ranking at the SHREC 2008 contest for 3D face recognition. We present an extensive comparative evaluation of the performance with the FRGC v2.0 data set and the SHREC08 data set.

A 3D face matching framework for facial curves

Among the many 3D face matching techniques that have been developed, are variants of 3D facial curve matching, which reduce the amount of face data to one or a few 3D curves. The face’s central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them has not been researched systematically. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in the literature, and select subsets of facial curves for effective and efficient face matching. With a set of eight geodesic contours we achieve a mean average precision (MAP) of 0.70 and 92.5% recognition rate (RR) on the 3D face retrieval track of the Shape Retrieval Contest (SHREC’08), and a MAP of 0.96 and 97.6% RR on the University of Notre Dame (UND) test set. Face matching with these curves is time-efficient and performs better than other sets of facial curves and depth map comparison.

Deformation Modeling for Robust 3D Face Matching

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both non-rigid deformations and pose changes (multiview) to a 3D face template. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built by combining the templates with synthesized deformations. The matching distance is computed by fitting this generative deformable model to a test scan. A fully automatic and prototypic 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy.