Face recognition based on fitting a 3D morphable model

Blanz, Volker; Vetter, Thomas

doi:10.1109/tpami.2003.1227983

Cited by 1,806 publications

(1,280 citation statements)

References 33 publications

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“…This method solved the pose and illumination problem with the aid of a 3D generic model and halved the unknown parameters according to the symmetric information. The most successful face recognition system across pose and lighting is the 3D morphable model [12]. In this method, the shape and the texture of a face are expressed as the barycentric coordinates as a linear combination of the shapes and textures of the exemplar faces respectively.…”

Section: Introductionmentioning

confidence: 99%

Pose Invariant Face Recognition Under Arbitrary Illumination Based on 3D Face Reconstruction

Chai¹,

Qing²,

Shan³

et al. 2005

Lecture Notes in Computer Science

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Abstract. Pose and illumination will be quite difference between probe and gallery images in many face recognition tasks. In this paper, a novel technique for face recognition under varying poses and lightings is proposed by calibrating the pose and light to a reference condition through an illumination invariant 3D face reconstruction. First, from single facial image, the elaborate 3D shape is recovered based on a statistical deformable model regressed through 2D geometry formed by some facial landmarks. Then with the recovered 3D shape, the texture independent on the illumination is generated by spherical harmonics ratio image and finally the illumination invariant 3D face is reconstructed completely. Our method combines the strength of statistical deformable model to describe the shape information and the compact representations of the illumination in spherical frequency space, and handle both the pose and illumination variation simultaneously. This algorithm can be used to synthesize novel views and enhance the performance of general face recognition. Some experimental results on CMU PIE database are presented.

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Section: Introductionmentioning

confidence: 99%

Pose Invariant Face Recognition Under Arbitrary Illumination Based on 3D Face Reconstruction

Chai¹,

Qing²,

Shan³

et al. 2005

Lecture Notes in Computer Science

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“…Solving for both simultaneously leads to a non-convex optimisation problem [25] which is notoriously difficult to solve. In this thesis, we instead propose estimating shape using geometric features alone (e.g.…”

Section: Contributionsmentioning

confidence: 99%

“…On the other hand, 3D approaches use face shape and reflectance data collected using face capture devices, allowing face intrinsics to be modelled directly. At the stage of fitting the model to image data, the forward rendering process must be simulated and extrinsic parameters estimated as part of the fitting process [25].…”

Section: State Of the Artmentioning

confidence: 99%

“…More recently, groupwise alignment has been used to automatically register samples of images using both stochastic [83] and minimum description length [84] approaches. In 3D, Blanz and Vetter [25] used a version of optical flow applied to cylindrical parameterisations of the face surfaces to establish dense correspondence between each face and a chosen template face (i.e. a pairwise approach).…”

Section: Statistical Face Modellingmentioning

confidence: 99%

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Inverse Rendering of Faces with a 3D Morphable Model

Aldrian

Smith

2013

IEEE Trans. Pattern Anal. Mach. Intell.

179

160

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In this thesis, we present a complete framework to inverse render faces with a 3D Morphable Model. By decomposing the image formation process into a geometric and photometric part, we are able to state the problem as a multilinear system which can be solved accurately and efficiently. As we treat each contribution as independent, the objective function is convex in the parameters and a globally optimal solution can be found. We start by recovering 3D shape using a novel algorithm which incorporates generalisation errors of the model obtained from empirical measurements. The algorithm is extended so it can efficiently deal with mixture distributions. We then describe three methods to recover facial texture, and for the second and third, diffuse lighting, specular reflectance and camera properties from a single image. These methods make increasingly weak assumptions and can all be solved in a linear fashion. We further modify our framework so it accounts for global illumination effects. This is achieved by incorporating statistical models for ambient occlusion and bent normals into the image formation model. We show that solving for ambient occlusion and bent normal parameters as part of the fitting process improves the accuracy of the estimated texture map and illumination environment. We present results on challenging data, rendered under complex natural illumination with both specular reflectance and occlusion of the illumination environment. We evaluate our findings on publicly available datasets, where we are able to obtain state-ofthe-art results. Finally, we present a practical method to synthesise a larger population from a small training-set and show how the new instances can be used to build a flexible PCA model.

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“…The actual output for a given input training pattern is determined by computing the outputs of units for each hidden layer in the forward pass of the input data. The error in the output is propagated backwards only to determine the weight updates [18].…”

Section: Back-propagation Learningmentioning

confidence: 99%

Neural Network Approach to Web Application Protection

Stephan¹,

Mohammed²,

Abbas³

2015

IJIET

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Abstract-The rapid growth of internet has created many services, which have become an integral part of our day in today life by using Web applications for making reservations, paying bills, and shopping on-line.The vulnerabilities in web application code provide an opportunity to the attack to be entre on applications level. Most network firewalls and antivirus software programs cannot stop attacks at the application level.In this paper, we have developed a prototypic web application firewall to detect new types of attacks that do not require signature updates, using a neural network back-propagation approach for identifying attacks that were not detected at the stage of signature analysis.The solution has been experimented on some parameters and some additional information about the user behaviors when the user accesses the web application and makes application-level control of the firewall in the framework of the scope of the WEB-application.The system is found to have good performance in comparing and matching the test patterns with already stored patterns and from (24) test data, (95%) success rate have been correctly recognized.Index Terms-Web applications firewall, signature, artificial neural network.

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Face recognition based on fitting a 3D morphable model

Cited by 1,806 publications

References 33 publications

Pose Invariant Face Recognition Under Arbitrary Illumination Based on 3D Face Reconstruction

Pose Invariant Face Recognition Under Arbitrary Illumination Based on 3D Face Reconstruction

Inverse Rendering of Faces with a 3D Morphable Model

Neural Network Approach to Web Application Protection

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