Karima Ouji

3D face recognition based on local shape patterns and sparse representation classifier

In recent years, 3D face recognition has been considered as a major solution to deal with these unsolved issues of reliable 2D face recognition, i.e. illumination and pose variations. This paper focuses on two critical aspects of 3D face recognition: facial feature description and classifier design. To address the former one, a novel local descriptor, namely Local Shape Patterns (LSP), is proposed. Since LSP operator extracts both differential structure and orientation information, it can describe local shape attributes comprehensively. For the latter one, Sparse Representation Classifier (SRC) is applied to classify these 3D shape-based facial features. Recently, SRC has been attracting more and more attention of researchers for its powerful ability on 2D image-based face recognition. This paper continues to investigate its competency in shape-based face recognition. The proposed approach is evaluated on the IV2 3D face database containing rich facial expression variations, and promising experimental results are achieved which prove its effectiveness for 3D face recognition and insensitiveness to expression changes.

3D Face Recognition Using R-ICP and Geodesic Coupled Approach

While most of existing methods use facial intensity images, a newest ones focus on introducing depth information to surmount some of classical face recognition problems such as pose, illumination, and facial expression variations. This abstract summarizes a new face recognition approach invariant to facial expressions based on dimensional surface matching. The core of our recognition/authentication scheme consists of aligning then comparing a probe face surface and gallery facial surfaces. In the off-line phase, we build the 3D face database with neutral expressions. The models inside include both shape and texture channels. In the on-line phase, a partial probe model is captured and compared either to all 3D faces in the gallery for identification scenario or compared to the genuine model for authentication scenario. The first step aligns probe and gallery models based only on static regions of faces within a new variant of the well known Iterative Closest Point called on R-ICP (Region-based Iterative Closest Point) which approximates the rigid transformations between the presented probe face and gallery one. R-ICP result is two matched sets of vertices in the both static and mimic regions of the face surfaces. For the second step, two geodesic maps are computed for the pair of vertices in the matched face regions. The recognition and authentication similarity score is based on the distance between these maps. Our evaluation experiments are done on 3D face dataset of IV2 french project.

3D Face Recognition using ICP and Geodesic Computation Coupled Approach

In this paper, we present a new face recognition approach based on dimensional surface matching. While most of existing methods use facial intensity images, a newest ones focus on introducing depth information to surmount some of classical face recognition problems such as pose, illumination, and facial expression variations. The presented matching algorithm is based first on ICP (Iterative Closest Point) that align rigidly facial surfaces and provides perfectly the posture of the presented probe model. Second, the similarity metric consists in computing geodesic maps on the overlapped parts of the aligned surfaces. The general paradigm consists in building a full 3D face gallery using a laser-based scanner (the on-line phase). At the on-line phase of identification or verification, a captured 2.5D face model (range image) is performed with the whole set of 3D faces from the gallery or compared to the 3D face model of the genuine, respectively. This probe model can be acquired from arbitrary viewpoint, with arbitrary facial expressions, and under arbitrary lighting conditions. Finally, We discuss some experimental results done on the ECL-IV2 new 3D face database.

A space-time depth super-resolution scheme for 3D face scanning

Current 3D imaging solutions are often based on rather specialized and complex sensors, such as structured light camera/projector systems, and require explicit user cooperation for 3D face scanning under more or less controlled lighting conditions. In this paper, we propose a cost effective 3D acquisition solution with a 3D space-time superresolution scheme which is particularly suited to 3D face scanning. The proposed solution uses a low-cost and easily movable hardware involving a calibrated camera pair coupled with a non calibrated projector device. We develop a hybrid stereovision and phase-shifting approach using two shifted patterns and a texture image, which not only takes advantage of the assets of stereovision and structured light but also overcomes their weaknesses. We carry out a new super-resolution scheme to correct the 3D facial model and to enrich the 3D scanned view. Our scheme performs the super-resolution despite facial expression variation using a CPD nonrigid matching. We demonstrate both visually and quantitatively the efficiency of the proposed technique.

Pattern Analysis for an Automatic and Low-cost 3D Face Acquisition Technique

This paper proposes an automatic 3D face modeling and localizing technique, based on active stereovision. In the offline stage, the optical and geometrical parameters of the stereosensor are estimated. In the online acquisition stage, alternate complementary patterns are successively projected. The captured right and left images are separately analyzed in order to localize left and right primitives with sub-pixel precision. This analysis also provides us with an efficient segmentation of the informative facial region. Epipolar geometry transforms a stereo matching problem into a one-dimensional search problem. Indeed, we employ an adapted, optimized dynamic programming algorithm to pairs of primitives which are already located in each epiline. 3D geometry is retrieved by computing the intersection of optical rays coming from the pair of matched features. A pipeline of geometric modeling techniques is applied to densify the obtained 3D point cloud, and to mesh and texturize the 3D final face model. An appropriate evaluation strategy is proposed and experimental results are provided.

3D Deformable Super-Resolution for Multi-Camera 3D Face Scanning

Low-cost and high-accuracy 3D face measurement is becoming increasingly important in many computer vision applications including face recognition, facial animation, games, orthodontics and aesthetic surgery. In most cases fringe projection based systems are used to overcome the relatively uniform appearance of skin. These systems employ a structured light camera/projector device and require explicit user cooperation and controlled lighting conditions. In this paper, we propose a 3D acquisition solution with a 3D space-time non-rigid super-resolution capability, using three calibrated cameras coupled with a non calibrated projector device, which is particularly suited to 3D face scanning, i.e. rapid, easily movable and robust to ambient lighting variation. The proposed solution is a hybrid stereovision and phase-shifting approach, using two shifted patterns and a texture image, which not only takes advantage of stereovision and structured light, but also overcomes their weaknesses. The super-resolution scheme involves a shape+texture 3D non-rigid registration for 3D artifacts correction in the presence of small non-rigid deformations as facial expressions.

Multi-camera 3D scanning with a non-rigid and space-time depth super-resolution capability

3D imaging sensors for the acquisition of three dimensional faces have created, in recent years, a considerable degree of interest for a number of applications. Structured light camera/projector systems are often used to overcome the relatively uniform appearance of skin. In this paper, we propose a 3D acquisition solution with a 3D space-time nonrigid super-resolution capability, using three calibrated cameras coupled with a non calibrated projector device, which is particularly suited to 3D face scanning, i.e. rapid, easily movable and robust to ambient lighting conditions. The proposed solution is a hybrid stereovision and phaseshifting approach, using two shifted patterns and a texture image, which not only takes advantage of the assets of stereovision and structured light but also overcomes their weaknesses. The super-resolution process is performed to deal with 3D artifacts and to complete the 3D scanned view in the presence of small non-rigid deformations as facial expressions. The experimental results demonstrate the effectiveness of the proposed approach.

Pattern-based Face Localization and Online Projector Parameterization for Multi-Camera 3D Scanning

3D face modeling is a very important and challenging task in computer vision especially in the presence of an expression variation. It plays an important role in a wide range of applications including facial animation, human-computer interaction and surgery. Current 3D face imaging solutions are often based on structured light camera/projector systems to overcome the relatively uniform appearance of skin. This paper proposes a space-time scheme for 3D face scanning, employing three calibrated cameras coupled with a non calibrated projector device. The proposed solution is a hybrid stereovision and phase-shifting approach, using two π-shifted sinusoid patterns and a texture image. It involves a pattern-based face localization approach and an online projector parameterization. The experimental results further validated the effectiveness of the proposed approach.