Journal Article•
3d face recognition under expressions, occlusions and pose variation
01 Jan 2018-International Journal of Advance Research and Innovative Ideas in Education (IJARIIE)-Vol. 4, Iss: 3, pp 906-926
TL;DR: A novel geometric framework for analysing 3D faces, with the specific goals of comparing, matching, and averaging their shapes is proposed and elastic shape analysis of these curves is used to develop a Riemannian framework for analyseing shapes of full facial surfaces.
Abstract: We propose a novel geometric framework for analysing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analysing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and illustrates the use of radial facial curves on 3D meshes to mode facial deformation caused by expression, occlusion and variation in poses and to recognize faces despite large expression, in presence of occlusion and pose variations. Here we represent facial surface by indexed collection of radial geodesic curves on 3D face meshes emanating from nose tip to the boundary of mesh and compare the facial shapes by comparing shapes of their corresponding curves. We use elastic shape analysis for comparing shapes of facial curves because elastic matching seems natural for facial deformation and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. Our results match or improve upon the state-of-the-art methods on two prominent databases: GavabDB and Bosporus, each posing a different type of challenges.
Citations
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TL;DR: This survey presents a state-of-the-art for 3D face recognition using local features, with the main focus being the extraction of these features.
137 citations
Book•
01 Jan 1974
124 citations
TL;DR: Experimental results on six challenging 3D facial datasets show that the proposed KMTS-TPWCRC framework achieves promising results for human face recognition with missing parts, occlusions, data corruptions, expressions and pose variations.
92 citations
Cites background or methods from "3d face recognition under expressio..."
...Our approach outperforms [15], with rank-1 IRs of 70....
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..., [15, 17]), the proposed approach is very e cient (see Section 5....
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...To achieve rigorous and fair comparison, we follow the same evaluation protocol as described in [15]....
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..., [15, 17]), the proposed approach does not require any...
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...Following a similar experimental setup as previous works [15], we manually detect the nosetip for these extreme cases in GavabDB....
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TL;DR: The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present, and suggests that the proposed N IRFaceNet method may be more suitable for non-cooperative-user applications.
Abstract: Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications
64 citations
TL;DR: A multilinear algorithm to automatically establish dense point-to-point correspondence over an arbitrarily large number of population specific 3D faces across identities, facial expressions and poses is presented.
60 citations
References
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TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,562 citations
"3d face recognition under expressio..." refers background or methods in this paper
...Kohonen [8] was the first to demonstrate that a neuron network could be used to recognize aligned and normalized faces....
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...In [8] feed forward neural network and back propagation neural networks are used in addition with PCA....
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Book•
02 Apr 2013
TL;DR: This book covers the general principles and ideas of designing biometric-based systems and their underlying tradeoffs, and the exploration of some of the numerous privacy and security implications of biometrics.
Abstract: Biometrics: Personal Identification in Networked Society is a comprehensive and accessible source of state-of-the-art information on all existing and emerging biometrics: the science of automatically identifying individuals based on their physiological or behavior characteristics. In particular, the book covers: *General principles and ideas of designing biometric-based systems and their underlying tradeoffs *Identification of important issues in the evaluation of biometrics-based systems *Integration of biometric cues, and the integration of biometrics with other existing technologies *Assessment of the capabilities and limitations of different biometrics *The comprehensive examination of biometric methods in commercial use and in research development *Exploration of some of the numerous privacy and security implications of biometrics. Also included are chapters on face and eye identification, speaker recognition, networking, and other timely technology-related issues. All chapters are written by leading internationally recognized experts from academia and industry. Biometrics: Personal Identification in Networked Society is an invaluable work for scientists, engineers, application developers, systems integrators, and others working in biometrics.
1,845 citations
TL;DR: A probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system.
Abstract: The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.
885 citations
"3d face recognition under expressio..." refers background in this paper
...Now, this two dimensional vector is changed to one dimensional vector [13]....
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Book•
16 Jun 2013
TL;DR: A study of Adaptive Neural Network Control System based on Differential Evolution Algorithm.
Abstract: A Study of Adaptive Neural Network Control System. Zhong, Heng Design of Fuzzy Logic Controller Based on Differential Evolution Algorithm. Shuai, Li (et al.). Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis. Fuzzy Logic and Neural Networks: Basic Concepts and Applications. logic genetic by rajasekaran ebook. srajasekaran and ga vijayalakshmi pai neural networks. MODERN MAGNETIC MATERIALS PRINCIPLES AND APPLICATIONS PDF FREE NETWORKS FUZZY LOGIC AND GENETIC ALGORITHMS SYNTHESIS.
508 citations
TL;DR: This paper presents the computational tools and a hardware prototype for 3D face recognition and presents the results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans.
Abstract: In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality
496 citations
"3d face recognition under expressio..." refers methods in this paper
...[4] utilize an annotated face model to study geometrical variability across faces....
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