Integrating Range and Texture Information for 3D Face Recognition

The performance of face recognition systems that use two-dimensional images depends on consistent conditions w.r.t. lighting, pose, and facial appearance. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary view, lighting, and facial appearance. For each subject, a 3D face model is constructed by integrating several 2.5D face scans from different viewpoints. A 2.5D scan is composed of one range image along with a registered 2D color image. 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 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. The 3D model in the gallery is used to synthesize new appearance samples with pose and illumination variations that are used for discriminant subspace analysis. The weighted sum rule is applied to combine the two matching components. A hierarchical matching structure is designed to further improve the system performance in both accuracy and efficiency. Experimental results are given for matching a database of 100 3D face models with 598 2.5D independent test scans acquired in different pose and lighting conditions, and with some smiling expression. The results show the feasibility of the proposed matching scheme. 1

Adaptive face modelling for reconstructing 3D face shapes from single 2D images

Example-based statistical face models using principle component analysis (PCA) have been widely deployed for three-dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA-based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been

3D FACE MODEL CONSTRUCTION BASED ON KINECT FOR FACE RECOGNITION

[[abstract]]We propose a simpler and faster method to recognize face. First, we use Kinect to detect frontal face and get depth image information with face, then we portrayed face in OpenGL to construct a three-dimensional face model based on the depth information. The face model also retains texture information of the original face images, and to create a complete change depth of face. It has a good result of repairing the distortion in side face. We can get a set face images with different angles by the method proposed, In recognition part, we use PCA(Principal Component Analysis) to reduce the dimensions, and classified with SVM(Support Vector Machine). The experiments show that the side face recognition can have good results.[[sponsorship]]National Taipei University[[conferencetype]]國際[[conferencedate]]20150718~20150719[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems

Noise modelling for denoising and 3D face recognition algorithms performance evaluation

This study proposes an algorithm is proposed to quantitatively evaluate the performance of three‐dimensional (3D) holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (i) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (ii) shows which parts of the face are more vulnerable to noise and require further post‐processing after data acquisition; and (iii) compares the performance of three different categories of recognition algorithms: training‐free matching‐based, subspace projection‐based and training‐based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Moreover, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training‐based (including the subspace projection) methods

Biometric Recognition of 3D Faces

Diplomová práce byla vypracována na studijním pobytu na "Gjovik University College" v Norsku, a je zpracována v angličtině. Tato práce se zabývá rozpoznáváním 3D obličejů. Je zde popsán obecný biometrický systém a také konkrétní postupy používané při rozpoznávání 2D i 3D obličejů. Následně je navžena metoda pro rozpoznávání 3D obličejů. Algoritmus je vyvíjen a testován pomocí databáze Face Recognition Grand Challenge (FRGC). Během předzpracování jsou nalezeny význačné body v obličeji a následně je trojrozměrný model zarovnán do referenční polohy. Dále jsou vstupní data porovnávána s biometrickými šablonami uloženými v databázi, a to je zajištěno využitím tří základních technik pro rozpoznávání obličejů -- metoda eigenface (PCA), rozpoznávání založené na histogramu obličeje a rozpoznávání založené na anatomických rysech. Nakonec jsou jednotlivé metody spojeny do jednoho systému, jehož celková výsledná výkonnost převyšuje výkonnost jednotlivých použitých technik.This Master's Thesis was performed during a study stay at the Gjovik University College, Norway. This Thesis is about biometric 3D face recognition. A general biometric system as well as specific techniques used in 2D and 3D face recognition are described. An automatic modular 3D face recognition method will be proposed. The algorithm is developed, tested and evaluated on the Face Recognition Grand Challenge (FRGC) database. During the preprocessing part, facial landmarks are located on the face surface and the three dimensional model is aligned to a predefined position. In the comparison module, the input probe scan is compared to the gallery template. There are three fundamental face recognition algorithms employed during the recognition pipeline -- the eigenface method (PCA), the recognition using histogram-based features, and the recognition based on the anatomical-Bertillon features of the face. Finally the decision module fuses the scores provided by the utilized recognition techniques. The resulting performance is better than any of utilized recognition algorithms.

Expression invariant face recognition using multi-stage 3D face fitting with 3D morphable face model

This paper aims to propose a new fully automated three-dimensional model based, real-time capable approach to recognize facial expressions from visual images of human faces in real time scenario. A multistage 3D fitting algorithm is applied with a morphable model to ensure the high accuracy and speed of the process in addition to eliminating the pose and illumination effects during the recognition process. The idea of the model is to update parameters at each stage in the fitting process. Feature extraction will be done using active appearance model while the feature classification will be done using the tree model to insure a good processing speed. This proposed model will show good results when shape, texture and extrinsic variations occur in the 3D domain since the combination of multistage fitting algorithm and tree model can enhance the speed and accuracy of the system recognition capabilities. This 3D morphable model algorithm can be widely used for 3D face analysis and 3D face recognition in real time scenarios

Face Recognition Algorithms

U diplomskom radu opisana je primjena algoritama za prepoznavanje lica u samom po- četku razvoja algoriama i danas. Opisani su glavni pristupi koji se koriste prilikom osmišljavanja algoritama kao što su psihloški i matematički pristup. Navedene su najbitnije sastavnice oba pristupa te je dan uvid u općeniti način pristupanja prepoznavanju lica. Algoritmi za prepoznavanje lica su u radu podijeljeni na dvije glavne kategorije - algoritmi koji koriste dvodimenzionalni i algoritmi koji koriste trodimenzionalni prikaz lica za prepoznavanje. Za svaku od kategorija navedena su po tri algoritma. Od dvodimenzionalnih algoritama opisane su Nixonova metoda, metoda Principal Component Analysis i Linear Discriminant Analysis. Metode algoritama koji koriste trodimenzionalni prikaz koje su opisane u radu su trodimenzionalno prepoznavanje bez rekonstrukcije lica, 3D Morphable Model i metoda bazirana na komponentama. Također, opisan je praktični dio rada u kojem je dana implemenatacija metode Principal Component Analysis i opisana je baza fotografija korištena u navedenoj implementaciji. U dijelovima isstraživanja ovoga rada korištena je FERET baza fotografija lica prikupljena u sklopu FERET programa, sponzorirana od strane ureda DOD Counterdrug Technology Development Program. Osim toga, pojašnjen je način korištenja implementiranog algoritmaThis thesis describes usage of face recognition algorithms since they first appeared until today. There is given insight into main approaches used in developing algorithms as psychological and mathematical approach. The most important components of both approaches are specified and there are explained general methods of face recognition. Face recognition algorithms are separated into two categories - algorithms using two-dimensional and three-dimensional face representations. There are described three methods for each category of algorithms. Among two-dimensional algorithms there are described Nixon method, Principal Component Analysis method and Linear Discriminant Method. Three-dimensional algorithms category consists of explanation od method for face recognition without face reconstruction, 3D Morphable Model method and method based on face components. Further more, there is explanation of practical part of this thesis where implementation of Principal Component Analysis method is given and there is description of images database used in implementation. Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office. Also, there is explained usage of implemented algorithm

Assessment of accuracy and recognition of three-dimensional computerized forensic craniofacial reconstruction

Facial reconstruction is a technique that aims to reproduce the individual facial characteristics based on interpretation of the skull, with the objective of recognition leading to identification. The aim of this paper was to evaluate the accuracy and recognition level of three-dimensional (3D) computerized forensic craniofacial reconstruction (CCFR) performed in a blind test on open-source software using computed tomography (CT) data from live subjects. Four CCFRs were produced by one of the researchers, who was provided with information concerning the age, sex, and ethnic group of each subject. The CCFRs were produced using Blender® with 3D models obtained from the CT data and templates from the MakeHuman® program. The evaluation of accuracy was carried out in CloudCompare, by geometric comparison of the CCFR to the subject 3D face model (obtained from the CT data). A recognition level was performed using the Picasa® recognition tool with a frontal standardized photography, images of the subject CT face model and the CCFR. Soft-tissue depth and nose, ears and mouth were based on published data, observing Brazilian facial parameters. The results were presented from all the points that form the CCFR model, with an average for each comparison between 63% and 74% with a distance -2.5 ≤ x ≤ 2.5 mm from the skin surface. The average distances were 1.66 to 0.33 mm and greater distances were observed around the eyes, cheeks, mental and zygomatic regions. Two of the four CCFRs were correctly matched by the Picasa® tool. Free software programs are capable of producing 3D CCFRs with plausible levels of accuracy and recognition and therefore indicate their value for use in forensic applications

Recognition of unfamiliar faces

People are excellent at identifying faces familiar to them, even from very low quality images, but are bad at recognising, or even matching, faces that are unfamiliar. In this review we shall consider some of the factors which affect our abilities to match unfamiliar faces. Major differences in orientation (e.g. inversion) or greyscale information (e.g. negation) affect face processing dramatically, and such effects are informative about the nature of the representations derived from unfamiliar faces, suggesting that these are based on relatively low-level image descriptions. Consistent with this, even relatively minor differences in lighting and viewpoint create problems for human face matching, leading to potentially important problems over the use of images from security video images. The relationships between different parts of the face (its "configuration") are as important to the impression created of an upright face as local features themselves, suggesting further constraints on the representations derived from faces. The review then turns to consider what computer face recognition systems may contribute to understanding both the theory and the practical problems of face identification. Computer systems can be used as an aid to person identification, but also in an attempt to model human perceptual processes. There are many approaches to computer recognition of faces, including ones based on low-level image analysis of whole face images, which have potential as models of human performance. Some systems show significant correlations with human perceptions of the same faces, for example recognising distinctive faces more easily. In some circumstances, some systems may exceed human abilities on unfamiliar faces. Finally, we look to the future of work in this area, that will incorporate motion and three-dimensional shape information