The Experts below are selected from a list of 45810 Experts worldwide ranked by ideXlab platform

Xiaoming Ren - One of the best experts on this subject based on the ideXlab platform.

Abdul Sattar - One of the best experts on this subject based on the ideXlab platform.

  • spiral search a hydrophobic core directed local search for simplified psp on 3d fcc lattice
    Asia-Pacific Bioinformatics Conference, 2013
    Co-Authors: Mahmood A Rashid, M Hakim A Newton, Tamjidul Hoque, Swakkhar Shatabda, Duc Nghia Pham, Abdul Sattar
    Abstract:

    Background Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-Dimensional Face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy.

Patrick J Flynn - One of the best experts on this subject based on the ideXlab platform.

  • Three-Dimensional Face imaging and recognition: a sensor design and comparative study
    2010
    Co-Authors: Patrick J Flynn, Robert Mckeon
    Abstract:

    Many commercially available 3D sensors suitable for Face image capture employ passive or texture-assisted stereo imaging or structured illumination with a moving light stripe. These techniques require a stationary subject. We describe a design and evaluation of a fixed-stripe, moving object 3D scanner designed for human Faces, called the Static Light Screen (SLS) Scanner (Patent Pending). Our method of acquisition requires the subject to walk through a light screen generated by two laser line projectors. Triangulation and tracking applied to the video sequences captured during subject motion yield a 3D image of the subject's Face from multiple images. To demonstrate the accuracy of our initial design, a small-scale facial recognition experiment was executed. In an experiment with 476 images of 161 unique subjects, we achieved 46.3% rank-one recognition using an Iterative Closest Point (ICP) based matching method, demonstrating the feasibility of the technique. This is the subset of our full SLS dataset with 2270 images of 379 unique subjects, which has a subject velocity less than 0.33 m/s. We also developed techniques to improve Face recognition based on ICP using fusion techniques and score normalization techniques. We improved rank-one recognition on a data set (FRGC v2) of 4007 Faces of 466 unique subjects from 96.4% rank-one recognition to 98.6%.

  • Improving three-Dimensional Face recognition model generation and biometrics
    2009
    Co-Authors: Patrick J Flynn, Christopher Bensing Boehnen
    Abstract:

    3D Face shape biometrics, with greater pose and lighting condition data invariance than 2D (photometric), has the potential to yield superior performance to 2D data for some applications. However, many of the capture limitations of 3D scanners are the same as those of 2D biometric capture devices with respect to lighting, environmental, and subject configurations. Many of the claimed advantages of 3D over 2D do not exist under current capture configurations. In addition, 3D scanners themselves are more expensive than 2D cameras, and 3D biometric data are unavailable on many of the subjects we would like to identify. Further, the significant computational cost of 3D Face recognition has made large scale deployment of 3D Face recognition impractical. The focus of this thesis is to address these issues to improve the feasability of 3D Face recognition so that it is more applicable outside of a research environment. In particular, the focus is on improving the methods and hardware needed to produce a 3D model of a Face, improving biometric recognition and verification performance, and decreasing the computational cost to allow for larger scale applications. In this thesis, I propose a new structure from motion approach, a new fast 3D Face biometric, and examine the impact of movement on existing structure from light devices.

  • Flexible and robust three-Dimensional Face recognition
    2008
    Co-Authors: Kevin W Bowyer, Patrick J Flynn, T.c. Faltemier
    Abstract:

    Face recognition is one of the least intrusive modalities in biometrics. Our research shows that using 3D Face data for recognition provides a promising route to improved performance. In this dissertation, we introduce a new system for 3D Face recognition that addresses many of the current challenges preventing this technology from becoming viable. The first challenge is to determine the feasibility of combining data from multiple sensors. Next, we create a technique named Region Ensemble for Face Recognition (REFER) that is capable of matching 3D Face images in the presence of expressions and occlusion. Accurate feature detection and pose recognition is accomplished through a novel technique that we have created, named Rotated Profile Signatures (RPS). Scalability issues are mitigated by combining a feature-based indexing technique with desktop grid processing to greatly reduce the amount of time required for recognition experiments. Finally, we investigate the potential benefits of using a multi-instance enrollment approach that can be used to further increase the performance of our system. These solutions result in a final system that is capable of deployment in a variety of realistic biometric scenarios.

  • EUSIPCO - Three-Dimensional Face and finger biometrics
    2004
    Co-Authors: K I Chang, Patrick J Flynn, Damon L. Woodard, Kevin W Bowyer
    Abstract:

    The use of dense range scans of the Face to supplement or supplant visible-light images in Face recognition systems has been a subject of recent interest in the biometrics community. This paper summarizes recent research activities designed to assess the potential of a high-resolution 3D Face scan as a viable biometric, using a large database of such scans. We show that 3D Faces offer potential for this application but also exhibit challenges that must be addressed before systems based on 3D Face can be fielded. We also describe preliminary results from a study of 3D hand shape biometrics employing curvature classification of the finger skin surFace.

  • a survey of approaches to three Dimensional Face recognition
    International Conference on Pattern Recognition, 2004
    Co-Authors: Kevin W Bowyer, K I Chang, Patrick J Flynn
    Abstract:

    The vast majority of Face recognition research has focused on the use of two-Dimensional intensity images, and is covered in existing survey papers. This survey focuses on Face recognition using three-Dimensional data, either alone or in combination with two-Dimensional intensity images. Challenges involved in developing more accurate three-Dimensional Face recognition are identified.

Ajmal Mian - One of the best experts on this subject based on the ideXlab platform.

  • Predicting sleep apnea from three-Dimensional Face photography.
    Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2020
    Co-Authors: Peter R. Eastwood, Syed Zulqarnain Gilani, Nigel Mcardle, David R. Hillman, Jennifer H. Walsh, Kathleen J. Maddison, Mithran S. Goonewardene, Ajmal Mian
    Abstract:

    Study Objectives:Craniofacial anatomy is recognized as an important predisposing factor in the pathogenesis of obstructive sleep apnea (OSA). This study used three-Dimensional (3D) facial surFace a...

Chun Chen - One of the best experts on this subject based on the ideXlab platform.

  • three Dimensional Face reconstruction from a single image by a coupled rbf network
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Mingli Song, Dacheng Tao, Xiaoqin Huang, Chun Chen
    Abstract:

    Reconstruction of a 3-D Face model from a single 2-D Face image is fundamentally important for Face recognition and animation because the 3-D Face model is invariant to changes of viewpoint, illumination, background clutter, and occlusions. Given a coupled training set that contains pairs of 2-D Faces and the corresponding 3-D Faces, we train a novel coupled radial basis function network (C-RBF) to recover the 3-D Face model from a single 2-D Face image. The C-RBF network explores: 1) the intrinsic representations of 3-D Face models and those of 2-D Face images; 2) mappings between a 3-D Face model and its intrinsic representation; and 3) mappings between a 2-D Face image and its intrinsic representation. Since a particular Face can be reconstructed by its nearest neighbors, we can assume that the linear combination coefficients for a particular 2-D Face image reconstruction are identical to those for the corresponding 3-D Face model reconstruction. Therefore, we can reconstruct a 3-D Face model by using a single 2-D Face image based on the C-RBF network. Extensive experimental results on the BU3D database indicate the effectiveness of the proposed C-RBF network for recovering the 3-D Face model from a single 2-D Face image.