High-quality 3D shape measurement with binarized dual phase-shifting method

ABSTRACT 3-D technology is commonplace in today\u27s world. They are used in many dierent aspects of life. Researchers have been keen on 3-D shape measurement and 3-D reconstruction techniques in past decades as a result of inspirations from dierent applications ranging from manufacturing, medicine to entertainment. The techniques can be broadly divided into contact and non-contact techniques. The contact techniques like coordinate measuring machine (CMM) dates way back to 1950s. It has been used extensively in the industries since then. It becomes predominant in industrial inspections owing to its high accuracy in the order of m. As we know that quality control is an important part of modern industries hence the technology is enjoying great popularity. However, the main disadvantage of this method is its slow speeds due to its requirement of point-by-point touch. Also, since this is a contact process, it might deform a soft object while performing measurements. Such limitations led the researchers to explore non-contact measurement technologies (optical metrology techniques). There are a variety of optical techniques developed till now. Some of the well-known technologies include laser scanners, stereo vision, and structured light systems. The main limitation of laser scanners is its limited speed due to its point-by-point or line-by-line scanning process. The stereo vision uses two cameras which take pictures of the object at two dierent angles. Then epipolar geometry is used to determine the 3-D coordinates of points in real-world. Such technology imitates human vision, but it had a few limitations too like the diculty of correspondence detection for uniform or periodic textures. Hence structured light systems were introduced which addresses the aforementioned limitations. There are various techniques developed including 2-D pseudo-random codication, binary codication, N-ary codication and digital fringe projection (DFP). The limitation of 2-D pseudo-random codication technique is its inability to achieve high spatial resolution since any uniquely generated and projected feature requires a span of several projector pixels. The binary codication techniques reduce the requirement of 2-D features to 1-D ones. However, since there are only two intensities, it is dicult to differentiate between the individual pixels within each black or white stripe. The other disadvantage is that n patterns are required to encode 2n pixels, meaning that the measurement speeds will be severely affected if a scene is to be coded with high-resolution. Dierently, DFP uses continuous sinusoidal patterns. The usage of continuous patterns addresses the main disadvantage of binary codication (i.e. the inability of this technique to differentiate between pixels was resolved by using sinusoid patterns). Thus, the spatial resolution is increased up to camera-pixel-level. On the other hand, since the DFP technique used 8-bit sinusoid patterns, the speed of measurement is limited to the maximum refreshing rate of 8-bit images for many video projectors (e.g. 120 Hz). This made it inapplicable for measurements of highly dynamic scenes. In order to overcome this speed limitation, the binary defocussing technique was proposed which uses 1-bit patterns to produce sinusoidal prole by projector defocusing. Although this technique has signicantly boosted the measurement speed up to kHz-level, if the patterns are not properly defocused (nearly focused or overly defocused), increased phase noise or harmonic errors will deteriorate the reconstructed surface quality. In this thesis research, two techniques are proposed to overcome the limitations of both DFP and binary defocusing technique: binarized dual phase shifting (BDPS) technique and Hilbert binarized dual phase shifting technique (HBDPS). Both techniques were able to achieve high-quality 3-D shape measurements even when the projector is not sufficiently defocused. The harmonic error was reduced by 47% by the BDPS method, and 74% by the HBDPS method. Moreover, both methods use binary patterns which preserve the speed advantage of the binary technology, hence it is potentially applicable to simultaneous high-speed and high-accuracy 3D shape measurements

Reconnaissance Biométrique par Fusion Multimodale de Visages

Biometric systems are considered to be one of the most effective methods of protecting and securing private or public life against all types of theft. Facial recognition is one of the most widely used methods, not because it is the most efficient and reliable, but rather because it is natural and non-intrusive and relatively accepted compared to other biometrics such as fingerprint and iris. The goal of developing biometric applications, such as facial recognition, has recently become important in smart cities. Over the past decades, many techniques, the applications of which include videoconferencing systems, facial reconstruction, security, etc. proposed to recognize a face in a 2D or 3D image. Generally, the change in lighting, variations in pose and facial expressions make 2D facial recognition less than reliable. However, 3D models may be able to overcome these constraints, except that most 3D facial recognition methods still treat the human face as a rigid object. This means that these methods are not able to handle facial expressions. In this thesis, we propose a new approach for automatic face verification by encoding the local information of 2D and 3D facial images as a high order tensor. First, the histograms of two local multiscale descriptors (LPQ and BSIF) are used to characterize both 2D and 3D facial images. Next, a tensor-based facial representation is designed to combine all the features extracted from 2D and 3D faces. Moreover, to improve the discrimination of the proposed tensor face representation, we used two multilinear subspace methods (MWPCA and MDA combined with WCCN). In addition, the WCCN technique is applied to face tensors to reduce the effect of intra-class directions using a normalization transform, as well as to improve the discriminating power of MDA. Our experiments were carried out on the three largest databases: FRGC v2.0, Bosphorus and CASIA 3D under different facial expressions, variations in pose and occlusions. The experimental results have shown the superiority of the proposed approach in terms of verification rate compared to the recent state-of-the-art method

Reconhecimento facial biométrico em nuvens de pontos tridimensionais

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2016.Recentemente, diversos processos de automação fazem uso de conhecimentos relacionados a visão computacional, utilizando-se das informações digitalizadas que auxiliam na tomada de decisões destes processos. O estudo de informações 3D é um assunto que vem sendo recorrente em comunidades de visão computacional e atividades gráficas. Uma gama de métodos vem sendo propostos visando obter melhores resultados de performance, em termos de acurácia e robustez. Neste trabalho realiza-se um processo de reconhecimento facial de posição frontal em uma base de dados contendo 31 sujeitos, em que cada sujeito apresenta 3 imagens de profundidade e 3 imagens de cor (RGB). As imagens de cor são utilizadas para detecção facial por uso de um Haar Cascade, que permite a extração dos pontos da face da imagem de profundidade formando uma nuvem de pontos tridimensional. Da nuvem de pontos foram extraídas a intensidade normal e a intensidade do índice de curvatura de cada ponto permitindo a formação de uma imagem bidimensional, intitulada de mapa de curvatura, a partir da qual extrai-se histogramas utilizados no processo de reconhecimento facial. A métrica utilizada para validar o desempenho do método trata-se da medida de F-Measure.Recently, many automation process make use of knowledge related to computer vision, exploiting digital information in form of images or data that assists the decision-making of these process. 3D data recognition is a trend topic in computer vision and graphics tasks community. A large scale of methods had been proposed for 3D applications, expecting a better performance in accuracy and robustness. In this paper a frontal face recognition process was accomplished in a 31 subject database, which presented 3 colorful images (RGB) and 3 depth images for each subject. The colorful images are utilized for face detection by a Haar Cascade algorithm, allowing the extraction of facial points in the depth image and the generation of a tridimensioinal point cloud. The point cloud is used to extract the normal intensity and the curvature index intensity of each point allowing the confection of a bidimensional image, entitled curvature map, of which histograms are extracted to perform the facial recognition task. The validation of the perfomance was fullfiled by the application of a F-Measure

Heritability of facial morphology

Facial recognition methodologies, widely used today in everything from automatic passport controls at airports to unlocking devices on mobile phones, has developed greatly in recent years. The methodologies vary from feature based landmark comparisons in 2D and 3D, utilising Principal Component Analysis (PCA) to surface-based Iterative Closest Point Algorithm (ICP) analysis and a wide variety of techniques in between. The aim of all facial recognition software (FCS) is to find or match a target face with a reference face of a known individual from an existing database. FCS, however, faces many challenges including temporal variations due to development/ageing and variations in facial expression. To determine any quantifiable heritability of facial morphology using this resource, one has to look for faces with enough demonstrable similarities to predict a possible genetic link, instead of the ordinary matching of the same individual’s face in different instances. With the exception of identical twins, this means the introduction of many more variables into the equation of how to relate faces to each other. Variation due to both developmental and degenerative aging becomes a much greater issue than in previous matching situations, especially when comparing parents with children. Additionally, sexual dimorphism is encountered with cross gender relationships, for example, between mothers and sons. Non-inherited variables are also encountered such as BMI, facial disfigurement and the effects of dental work and tooth loss. For this study a Trimmed Iterative Closest Point Algorithm (TrICP) was applied to three-dimensional surfaces scans, created using a white light scanner and Flexscan 3D, of the faces of 41 families consisting of 139 individuals. The TrICP algorithm produced 7176 Mesh-to-mesh Values (MMV) for each of seven sections of the face (Whole face, Eyes, Nose, Mouth, Eyes-Nose, Eyes-Nose-Mouth, and Eyes-Nose- Mouth-Chin). Receiver Operated Characteristic (ROC) analysis was then conducted for each of the seven sections of the face within 11 predetermined categories of relationship, in order to assess the utility of the method for predicting familial relationships (sensitivity/specificity). Additionally, the MMVs of three single features, (eyes, nose and mouth) were combined to form four combination areas which were analysed within the same 11 relationship categories. Overall the relationship between sisters showed the most similarity across all areas of the face with the clear exception of the mouth. Where female to female comparison was conducted the mouth consistently negatively affected the results. The father-daughter relationship showed the least similarity overall and was only significant for three of the 11 portions of the face. In general, the combination of three single features achieved greater accuracy as shown by Areas Under the Curve (AUC) than all other portions of the face and single features were less predictive than the face as a whole