3D Face Modeling: Comprehensive Description

This article presents the topic of Three Dimensional facial reconstruction approaches and some used methods. In this paper, we implement three-dimensional facial reconstruction algorithms based on various face databases using single image as an input and analyzing their performance on several aspects.Researchers proposed many applications for this issue, but most have their drawbacks and limitations. Secondly, we discuss about three-dimensional shapes and models based on facial techniques in detail. It concludes with an analysis of several implementations and with some technical discussions about 3D facial reconstruction

2004

An analysis-by-synthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace; Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) the proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant PIE.

Guide to Biometric …, 2009

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.

Engineering Applications of Artificial Intelligence, 2009

Applications related to game technology, law-enforcement, security, medicine or biometrics are becoming increasingly important, which, combined with the proliferation of three-dimensional (3D) scanning hardware, have made that 3D face recognition is now becoming a promising and feasible alternative to 2D face methods. The main advantage of 3D data, when compared with traditional 2D approaches, is that it provides information that is invariant to rigid geometric transformations and to pose and illumination conditions. One key element for any 3D face recognition system is the modeling of the available scanned data. This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on Support Vector Machines and another one on Principal Component Analysis (with a Euclidean classifier). Also, two types of environments were tested in order to check the robustness of the proposed models: a controlled environment with respect to facial conditions (i.e. expressions, face rotations, etc) and a noncontrolled one (presenting face rotations and pronounced facial expressions). The recognition rates obtained using reduced spatial resolution representations (a 77.86 % for non-controlled environments and a 90.16% for controlled environments, respectively) show that the proposed models can be effectively used for practical face recognition applications.

Vision Geometry XV, 2007

3D has become an important modality for face biometrics. The accuracy of a 3D face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a one-to-all registration approach, which means each new facial surface is registered to all faces in the gallery, at a great

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

The performance of face recognition systems that use twodimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. We are developing a multi-view face recognition system that utilizes three-dimensional (3D) information about the face to make the system more robust to these variations. This paper describes a procedure for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views, using coordinate system invariant properties of the facial surface. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. A robust similarity metric is defined for matching, based on an Iterative Closest Point (ICP) registration process. Results are given for matching a database of 18 3D face models with 113 2.5D face scans.

Biometrics is the area of bioengineering that pursues the characterization of individuals in a population (e.g., a particular person) by means of something that the individual is or produces. Among the different modalities in biometrics, face recognition has been a focus in research for the last couple of decades because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent to 2D face recognition, i.e. sensitivity to illumination conditions and positions of a subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: expression recognition system, expressional face recognition system and neutral face recognition system. A system for the recognition of faces with one type of expression (smile) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

In this paper, we did a review on some of the existing methods for classification and recognition of human faces. We have had a small discussion on a few algorithms like PCA, support vector machine, ID3, etc. and we have also discussed about some of the existing work done by my fellow researchers. We have also proposed a method for face detection using 3D modeling of image. We will produce a 3D face model then we do the refinement of 3D face we receive by editing of points and finally we perform a smoothing process. Smoothing of image is a performed to get the more realistic 3D face model for the person.

pphmj.com

Face recognition is one of the most intensively studied topics in the field of computer vision and pattern recognition. In this paper, two statistical models of facial shadow and shape, embedded within a shape-from-shading (SFS) algorithm, are used to ...

Handbook of Remote …, 2009

3D face recognition has received a lot of attention in the last decade, leading to improved sensors and algorithms that promise to enable large-scale deployment of biometric systems that rely on this modality. This chapter discusses advances in 3D face recognition with respect to current research and technology trends, together with its open challenges. Five real-world scenarios are described for application of 3D face biometrics. Then we provide a comparative overview of the currently available commercial sensors, and point out to research databases acquired with each technology. The algorithmic aspects of 3D face recognition are broadly covered; we inspect automatic landmarking and automatic registration as sine qua non parts of a complete 3D facial biometric system. We summarize major coordinated actions in evaluating 3D face recognition algorithms, and conclude with a case study on a recent and challenging database.

3D Research, 2010

The goal of this paper is to presents Brief Description of literature on Image Based human and machine recognition of faces during 1987 to 2010. Machine recognition of faces has several applications. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. In addition, relevant topics such as Brief studies, system evaluation, and issues of illumination and pose variation are covered. In this paper numerous method which related to image based 3D face recognition are discussed.

2006

Abstract 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

Face recognition is one of the most hot and challengeable technologies, which is based on biometrics, and also one of the most potential technologies. As the most natural and friendly identification method, automatic face recognition has become the important part of the next generation computing technology. 3D face recognition methods are able to overcome the problems resulting from illumination, expression or pose variations in 2D face recognition. Facial feature mainly concentrate on the eyes, nose and mouth, therefore, this paper mainly detects the characteristics of the three regions in the human face, then calculate the geometric characteristics of human face based on these characteristics point, including the straight-line Euclidean distance, curvature distance, area, angle and volume. The main contributions of the work is that the curve distance of two key feature points is added into the feature vector, which consists of Euclidean distance, curve distance, angle and volume. Experiment results show that the algorithm can recognize faces effectively.

13th European Signal Processing …, 2005

Face recognition (FR) is the preferred mode of identity recognition by humans: It is natural, robust and unintrusive. However, automatic FR techniques have failed to match up to expectations: Variations in pose, illumination and expression limit the performance of 2D FR techniques. In recent years, 3D FR has shown promise to overcome these challanges. With the availability of cheaper acquisition methods, 3D face recognition can be a way out of these problems, both as a stand-alone method, or as a supplement to 2D face recognition. We review the relevant work on 3D face recognition here, and discuss merits of different representations and recognition algorithms.