A robust analysis, detection and recognition of facial features in 2.5D images

Bagchi, Parama; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/s11042-015-2835-7

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…The study 24 proposed an algorithm to detect seven landmark points on the face. This study is focused on employing feature points determined using Euclidean distance between pairs of the detected landmark points.…”

Section: -D Face Recognitionmentioning

confidence: 99%

Three-dimensional face recognition using variance-based registration and subject-specific descriptors

Ratyal

Taj

Sajid

et al. 2019

International Journal of Advanced Robotic Systems

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Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.

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Section: -D Face Recognitionmentioning

confidence: 99%

Three-dimensional face recognition using variance-based registration and subject-specific descriptors

Ratyal

Taj

Sajid

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Ali et al [30] proposed a new image retrieval method that utilized the SVM for normalized histograms which is constructed by using the visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). SVM was also applied to 3D face recognition on GavabDB database with an accuracy of 87.5% [31,32]. Besides, a method using SVM was proposed for satellite image classification based on Pairs Orthogonal Vector Histogram (POVH).…”

Section: Related Workmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

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In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

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“…Application areas include computer graphics and visualization, healthcare, seismology, computational archaeology [Du18], etc. For example, curvature has been used in segmentation, object recognition, geometric modeling, and analysis of images and volumes [Bib16,Bes86,Bel12,Bag16,Lef18,Sou16], to perform reconstruction in images [Lef17], for biometrics [Sya17], for computer vision-based quality control in manufacturing [Kot18], etc. Other examples include emphasizing features in renderings of meshes [AR18] and images [Hau18], mesh parameterization [Vin17], highlighting shapes in volume renderings [Kin03], and visualization of medical data [Pre16].…”

Section: Introductionmentioning

confidence: 99%

Exhibition and Evaluation of Two Schemes for Determining Hypersurface Curvature in Volumetric Data

Hauenstein

Newman

2019

JWSCG

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Advancements in methodologies for determining 3-dimensional manifold (hypersurface) curvature in volumetric data are presented. Such determinations are requisite in certain shape-based visualization and analysis tasks. The methods explored here are convolution-based approaches. In addition to motivating and describing these methods, an evaluation of their (1) accuracy and (2) computational performance is also presented. That evaluation includes benchmarking on both noise-free and noisy volumetric data.

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A robust analysis, detection and recognition of facial features in 2.5D images

Cited by 13 publications

References 21 publications

Three-dimensional face recognition using variance-based registration and subject-specific descriptors

Three-dimensional face recognition using variance-based registration and subject-specific descriptors

A novel parallel learning algorithm for pattern classification

Exhibition and Evaluation of Two Schemes for Determining Hypersurface Curvature in Volumetric Data

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