Identity Management in Face Recognition Systems

Tistarelli, Mássimo; Grosso, Enrico

doi:10.1007/978-3-540-89991-4_8

Cited by 7 publications

(4 citation statements)

References 71 publications

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“…The statistic-based methods include: 1) Sample-based methods: Maximum Mean Discrepancy (MMD) [17]; 2)Subspace-based methods: Covariance Discriminant Learning (CDL) [15] 3) Distribution-based methods: Gaussian Mixture Model(GMM) [3] and Single 225 Gaussian Model (SGM) [2].…”

Section: Methodsmentioning

confidence: 99%

“…In gait recognition, for example, frame by frame static features of a certain object are considered as a feature set. Similarly in human action recognition, spatialtemporal features uniformly extracted from frames of an action atom are considered as [2,3]. The task of feature set classification is to classify an input feature set to one of the sets in the training gallery [4].…”

Section: Introductionmentioning

confidence: 99%

“…In general, three types of statistics have been commonly applied on set model- 15 ing, i.e. sample-based statistics (SAS) [6,7,8,9], subspace-based statistics (SUS) [10,11,12,13,14,15,16] and distribution-based statistics (DIS) [2,3]. Utilizing affine transformation and centroid of samples, sample-based statistics represent d-dimension feature sets with first-order statistics in the R d space.…”

Section: Introductionmentioning

confidence: 99%

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Statistical adaptive metric learning in visual action feature set recognition

Dai

Man

2016

Image and Vision Computing

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Great variances in visual features often present significant challenges in human action recognitions. To address this common problem, this paper proposes a statistical adaptive metric learning (SAML) method by exploring various selections and combinations of multiple statistics in a unified metric learning framework. Most statistics have certain advantages in specific controlled environments, and systematic selections and combinations can adapt them to more realistic "in the wild" scenarios. In the proposed method, multiple statistics, include means, covariance matrices and Gaussian distributions, are explicitly mapped or generated in the Riemannian manifolds. Typically, d-dimensional mean vectors in R d are mapped to a R d×d space of symmetric positive definite (SPD) matrices Sym + d . Subsequently, by embedding the heterogeneous manifolds in their tangent Hilbert space, subspaces combination with minimal deviation is selected from multiple statistics. Then Mahalanobis metrics are introduced to map them back into the Euclidean space. Unified optimizations are finally performed based on the Euclidean distances. In the proposed method, subspaces with smaller deviations are selected before metric learning. Therefore, by exploring different metric combinations, the final learning is more representative and effective than exhaustively learning from all the hybrid metrics. Experimental evaluations are conducted on human action recognitions in both static and dynamic scenarios. Promising results demonstrate that the proposed method performs effectively for human action recognitions in the wild. a feature set [1]. In addition, image sets have been commonly used in face recognitions 30 recognitions can finally be achieved by using Nearest Neighbors (NNs) method. Secondorder statistics based methods have better representation of the data, but it is hard to design a discriminant function with a unified distance measure for the manifolds. Typical subspace-based methods include Mutual Subspace Method (MSM) [11], Discriminant Canonical Correlations (DCC)[10], Manifold Discriminant Analysis (MDA) [14], Grass-35 mann Discriminant analysis (GDA)[13], Covariance Discriminative Learning (CDL)[15], Localized Multi-Kernel Metric Learning (LMKML)[16] etc. Distribution based statistics model each sample in the feature set with a distribution, which can be expressed as an expansion of the Riemannian manifold from the 2nd-order statistic space Sym + d to Sym + d+1 . Such methods are often with 3rd-order statistics and may lead to complex parametric 40 distribution comparison. Typical examples include Single Gaussian Models (SGM) [2], Gaussian Mixture Models (GMM) [3] and kernel version of ITML with DIS-based set model (DIS-ITML). Although 3rd-order statistics model sets with more consolidated representations, the hypothesis tests often require significant amount of computation in distribution comparisons. 45More adaptive forms of set modeling methods have been proposed by combining multiple statistical metrics in certain heuristic ways....

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical adaptive metric learning in visual action feature set recognition

Dai

Man

2016

Image and Vision Computing

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“…Face recognition [1][2][3][4][5], as a biometric recognition technology, is one of the hot topics in the research fields of pattern recognition, image processing, machine vision, neural networks, and cognitive science in recent years. At the same time, face recognition, as a biometric identification technology with high stability, high accuracy, difficult to copy, and easy to be accepted by humans [6][7][8][9], has a wide range of application prospects in the fields of identity authentication, security monitoring, human-computer interaction, etc. With the increasing innovation of information technology, the processing of images by face recognition technology has become more and more complex.…”

Section: Introductionmentioning

confidence: 99%

Application of Multiscale Facial Feature Manifold Learning Based on VGG‐16

Zhu

Liu

et al. 2021

Journal of Sensors

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Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.

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Statistical Adaptive Metric Learning for Action Feature Set Recognition in the Wild

Dai

Man

2015

Advances in Visual Computing

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Identity Management in Face Recognition Systems

Cited by 7 publications

References 71 publications

Statistical adaptive metric learning in visual action feature set recognition

Statistical adaptive metric learning in visual action feature set recognition

Application of Multiscale Facial Feature Manifold Learning Based on VGG‐16

Statistical Adaptive Metric Learning for Action Feature Set Recognition in the Wild

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