Automatic 3D Face Recognition Using Fourier Descriptors

Elyan, Eyad; Ugail, Hassan

doi:10.1109/cw.2009.48

Cited by 5 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Starting from this concept, Elyan et al [11] presented a technique for 3D face recognition system using a set of parameters representing the central region of the face. These parameters are essentially vertical and cross sectional profiles and are extracted automatically without any prior knowledge or assumption about the image pose or orientation.…”

Section: Introductionmentioning

confidence: 99%

Geometrical descriptors for human face morphological analysis and recognition

Vezzetti

Marcolin

2012

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Geometrical descriptors for human face morphological analysis and recognition

Vezzetti

Marcolin

2012

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…The method is invariant to pose and illumination, but the performance of the method closely depend on the accuracy of "control points" localization. In the approach proposed by (Elyan & Ugail, 2009), the first goal was to automatically determine the symmetry profile along the face. This was 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.…”

Section: Geometric Approachmentioning

confidence: 99%

“…Coon's patch is a parametric surface defined by a given four-boundary curves. In (Elyan & Ugail, 2009), the four boundaries of the coon's patch were determined based on a boundary curve that encloses an approximated central region of interest, which is simply the region of the face that contains or likely to contain the nose area. This region was approximated based on the centre of the mass that represents the 3D facial image.…”

Section: Geometric Approachmentioning

confidence: 99%

3D Face Recognition

Zaeri¹

2011

New Approaches to Characterization and Recognition of Faces

View full text Add to dashboard Cite

IntroductionBiometric systems for human recognition are an ongoing demand. Among all biometric technologies which are employed so far, face recognition is one of the most widely outspread biometrics. Its daily use by nearly everyone as the primary mean for recognizing other humans and its naturalness have turned face recognition into a well-accepted method. Furthermore, this image procurement is not considered as intrusive as the other mentioned alternatives. Nonetheless, in spite of the various facial recognition systems which already exist, many of them have been unsuccessful in matching up to expectations. 2D facial recognition systems are constrained by limitations such as physical appearance changes, aging factor, pose and changes in lighting intensity. Recently, to overcome these challenges 3D facial recognition systems have been issued as the newly emerged biometric technique, showing a high level of accuracy and reliability, being more robust to face variation due to the different factors. A face-based biometric system consists of acquisition devices, preprocessing, feature extraction, data storage and a comparator. An acquisition device may be a 2D-, 3D-or an infra-red-camera that can record the facial information. The preprocessing can detect facial landmarks, align facial data and crop facial area. It can filter irrelevant information such as hair, background and reduce facial variation due to pose change. In 2D images, landmarks such as eye, eyebrow, mouths etc, can be reliably detected, in contrast, nose is the most important landmark in 3D face recognition. The 3D information (depth and texture maps) corresponding to the surface of the face may be acquired using different alternatives: A multi camera system (stereoscopy), range cameras or 3D laser and scanner devices. Different approaches have been presented from the 3D perspective. The first approach would correspond to all 3D approaches that require the same data format in the training and in the testing stage. The second philosophy would enclose all approaches that take advantage of the 3D data during the training stage but then use 2D data in the recognition stage. Approaches of the first category report better results t h a n o f t h e s e c o n d g r o u p ; h o w e v e r , t h e m a i n d r a w b a c k o f t h i s c a t e g o r y i s t h a t t h e acquisition conditions and elements of the test scenario should be well synchronized and controlled in order to acquire accurate 3D data. Thus, they are not suitable for surveillance applications or control access points where only one "normal" 2D texture image (from any view) acquired from a single camera is available. The second category encloses model-based approaches. Nevertheless, model-based face recognition approaches present the main drawback of a high computational burden required to fit the images to the 3D models. www.intechopen.com New Approaches to Characterization and Recognition of Faces 48In this chapter, we study 3D face recognition where we provide a description of the most recent ...

show abstract

“…Elyan and Ugail [9] presented a method to determine the symmetry profile of the face. They computed the intersection between the symmetry plane and the facial mesh and then computed a few feature points along the symmetry profile in order to allocate the central region of the face and extract a set of profiles from that region.…”

Section: D Facial Recognition Studiesmentioning

confidence: 99%

“…To locate the tip of the nose they fit a bilinear blended Coon's surface patch. Coon's patch is simply a parametric surface defined by four given boundary curves [9]. These four boundaries of the Coon's patch are determined based on a boundary curve that encloses an approximated central region of interest, which is simply the region of the face that contains or is likely to contain the nose area [3].…”

Section: D Facial Recognition Studiesmentioning

confidence: 99%

Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study

Amman¹,

Jordan²

2015

JCC

View full text Add to dashboard Cite

This paper explores the possibility of using multi-core programming model that implements the Cascade correlation neural networks technique (CCNNs), to enhance the classification phase of 3D facial recognition system, after extracting robust and distinguishable features. This research provides a comprehensive summary of the 3D facial recognition systems, as well as the state-of-theart for the Parallel Cascade Correlation Neural Networks methods (PCCNNs). Moreover, it highlights the lack of literature that combined between distributed and shared memory model which leads to novel possibility of taking advantage of the strengths of both approaches in order to construct an efficient parallel computing system for 3D facial recognition.

show abstract

Automatic 3D Face Recognition Using Fourier Descriptors

Cited by 5 publications

References 21 publications

Geometrical descriptors for human face morphological analysis and recognition

Geometrical descriptors for human face morphological analysis and recognition

3D Face Recognition

Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study

Contact Info

Product

Resources

About