3D face recognition based on machine learning

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Biometrics in forensic science: challenges, lessons and new technologies

Biometrics has historically found its natural mate in Forensics. The first applications found in the literature and over cited so many times, are related to biometric measurements for the identification of multiple offenders from some of their biometric and anthropometric characteristics (tenprint cards) and individualization of offender from traces found on crime-scenes (e.g. fingermarks, earmarks, bitemarks, DNA). From sir Francis Galton, to the introduction of AFIS systems in the scientific laboratories of police departments, Biometrics and Forensics have been "dating" with alternate results and outcomes. As a matter of facts there are many technologies developed under the "Biometrics umbrella" which may be optimised to better impact several Forensic scenarios and criminal investigations. At the same time, there is an almost endless list of open problems and processes in Forensics which may benefit from the introduction of tailored Biometric technologies. Joining the two disciplines, on a proper scientific ground, may only result in the success for both fields, as well as a tangible benefit for the society. A number of Forensic processes may involve Biometric-related technologies, among them: Evidence evaluation, Forensic investigation, Forensic Intelligence, Surveillance, Forensic ID management and Verification.\ud The COST Action IC1106 funded by the European Commission, is trying to better understand how Biometric and Forensics synergies can be exploited within a pan-European scientific alliance which extends its scope to partners from USA, China and Australia.\ud Several results have been already accomplished pursuing research in this direction. Notably the studies in 2D and 3D face recognition have been gradually applied to the forensic investigation process. In this paper a few solutions will be presented to match 3D face shapes along with some experimental results

Automatic features characterization from 3d facial images.

This paper presents a novel and computationally fast method for automatic identification of symmetry profile from 3D facial images. The algorithm is based on the concepts of computational geometry which yield fast and accurate results. In order to detect the symmetry profile of a human face, the tip of the nose is identified first. Assuming that the symmetry plane passes through the tip of the nose, the symmetry profile is then extracted. This is undertaken by means of computing the intersection between the symmetry plane and the facial mesh, resulting in a planner curve that accurately represents the symmetry profile. Experimentation using two different 3D face databases was carried out, resulting in fast and accurate results

3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques.

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods

Feature extraction for range image interpretation using local topology statistics

This thesis presents an approach for interpreting range images of known subject matter, such as the human face, based on the extraction and matching of local features from the images. In recent years, approaches to interpret two-dimensional (2D) images based on local feature extraction have advanced greatly, for example, systems such as Scale Invariant Feature Transform (SIFT) can detect and describe the local features in the 2D images effectively. With the aid of rapidly advancing three-dimensional (3D) imaging technology, in particular, the advent of commercially available surface scanning systems based on photogrammetry, image representation has been able to extend into the third dimension. Moreover, range images confer a number of advantages over conventional 2D images, for instance, the properties of being invariant to lighting, pose and viewpoint changes. As a result, an attempt has been made in this work to establish how best to represent the local range surface with a feature descriptor, thereby developing a matching system that takes advantages of the third dimension present in the range images and casting this in the framework of an existing scale and rotational invariance recognition technology: SIFT. By exploring the statistical representations of the local variation, it is possible to represent and match range images of human faces. This can be achieved by extracting unique mathematical keys known as feature descriptors, from the various automatically generated stable keypoint locations of the range images, thereby capturing the local information of the distributions of the mixes of surface types and their orientations simultaneously. Keypoints are generated through scale-space approach, where the (x,y) location and the appropriate scale (sigma) are detected. In order to achieve invariance to in-plane viewpoint rotational changes, a consistent canonical orientation is assigned to each keypoint and the sampling patch is rotated to this canonical orientation. The mixes of surface types, derived using the shape index, and the image gradient orientations are extracted from each sampling patch by placing nine overlapping Gaussian sub-regions over the measurement aperture. Each of the nine regions is overlapped by one standard deviation in order to minimise the occurrence of spatial aliasing during the sampling stages and to provide a better continuity within the descriptor. Moreover, surface normals can be computed from each of the keypoint location, allowing the local 3D pose to be estimated and corrected within the feature descriptors since the orientations in which the images were captured are unknown a priori. As a result, the formulated feature descriptors have strong discriminative power and are stable to rotational changes

Abstract Comparing and combining depth and texture cues for face recognition *

Depth and texture play complementary roles in the coding of faces. While most existing face recognition methods are based on texture cues from intensity images due to their ease of acquisition, more accurate face recognition can be achieved by exploiting both modalities. To evaluate this claim, we have designed a pattern classifier for three different inputs: (1) depth map, (2) texture map, and (3) both depth and texture maps of the facial surface. Two existing face recognition methods are considered for this task, FaceIt technology and FisherFaces. Recognition performance is evaluated based on face data of 185 subjects, captured with the structured-light-based Rainbow250 3D camera. q 2004 Elsevier B.V. All rights reserved