An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.

This paper presents a theory and practical computations for visual age classification from facial images. Currently, the theory has only beenimplemented to classify input images into one of three agegroups: babies, young adults, and senior adults. The computations are based on cranio-facial development theory and skin wrinkle analysis. In the implementation, primary features of the face are found first, followed by secondary feature analysis. The primary features are the eyes, nose, mouth, chin, virtual-top of the head and the sides of the face. From these features, ratios that distinguish babies from young adults and seniors are computed. In secondary feature analysis, a wrinkle geography map is used to guide the detection and measurement of wrinkles. The wrinkle index computed is sufficient to distinguish seniors from young adults and babies. A combination rule for the ratios and the wrinkle index thus permits categorization of a face into one of three classes. Results using real images are presented. This is the first work involving age classification, and the first work that successfully extracts and uses natural wrinkles. It is also a successful demonstration that facial features are sufficient for a classification task, a finding that is important to the debate about what are appropriate representations for facial analysis. c 1999 Academic Press 1.

www.elsevier.com/locate/cviu A survey of approaches and challenges in

3D data has many advantages over image data. It is robust to illumination change and does not have a scaling problem caused by distance of an object. Also it can be viewed at various angles. Nowadays with advance of 3D capturing tools, laser scanners and high-speed stereo machines, interests in 3D data processing have been increased. But the number of 3D face recognition approaching is still little. The method of normalization and real-time feature extraction of 3D face data(range data) is presented in this paper. The step of normalization of range data is performed first using the symmetry of the defined facial section pattern and characteristics of changes of the pattern according to head rotations. Normalization of the data for head rotations can not only give strong constraints on the positions of facial features but also reduce the dimension of parameters used in the deformable template matching which is done at the next step. Facial features are found in a range image, which is obtained by projection of the normalized range data, using the deformable templates of eyes, nose and mouth. For reliable feature detection surface curvatures, which can represent a local surface shape, are used in this step. We define the energy functions of each template and the conditions of major control points using curvature information. Finally the facial features are positioned in three-dimensional space by back-mapping to the original range data. The back-mapping is the inverse process of getting the facial range image. 1

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.

Automated human face recognition is required in numerous applications. While considerable progress has been made in color/two dimensional (2D) face recognition, three dimensional (3D) face recognition technology is much less developed. 3D face recognition approaches based on the appearance of range images and geometric properties of the facial surface have been proposed. Methods that combine 2D and 3D modalities also exist. These innovations have advanced the field and have created novel areas of investigation. The purpose of this chapter is to provide a summary and critical analysis of the progress in 3D and 2D+3D face recognition. The chapter also identifies open problems and directions for future work in the area. 2

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.

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In this dissertation, we focus on two related parts of a 3D face recognition system with wireless transportation. In the first part, the core components of the system, namely, the feature extraction and classification component, are introduced. In the feature extraction component, range images are taken as inputs and processed in order to extract features. The classification component uses the extracted features as inputs and makes classification decisions based on trained classifiers. In the second part, we consider the wireless transportation problem of range images, which are captured by scattered sensor nodes from target objects and are forwarded to the core components (i.e., feature extraction and classification components) of the face recognition system. Contrary to the conventional definition of being a transducer, a sensor node can be a person, a vehicle, etc. The wireless transportation component not only brings flexibility to the system but also makes the “proactive ” face recognition possible. For the feature extraction component, we first introduce the 3D Morphable Model. Then a 3D feature extraction algorithm based on the 3D Morphable Model is presented. The algorithm is insensitive to facial expression. Experimental results