Secure and Robust Iris Recognition Using Random Projections and Sparse Representations

Pillai, Jaishanker K.; Patel, Vishal M.; Chellappa, Rama; Ratha, Nalini K.

doi:10.1109/tpami.2011.34

Cited by 293 publications

(165 citation statements)

References 47 publications

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“…For example, compressive classification has been explored with using a small number of random measurements to achieve classification without reconstruction [17]. The sparse representation algorithm has been applied to biometric recognition problems and is surprisingly robust [61,45]. Sparse approximation applied to semantic hierarchies [41] has been shown to be efficient in categorizing between large numbers of classes [35].…”

Section: Related Workmentioning

confidence: 99%

Sparse Sensor Placement Optimization for Classification

Brunton¹,

Brunton²,

Proctor³

et al. 2016

SIAM J. Appl. Math.

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Abstract. Choosing a limited set of sensor locations to characterize or classify a high-dimensional system is an important challenge in engineering design. Traditionally, optimizing the sensor locations involves a brute-force, combinatorial search, which is NP-hard and is computationally intractable for even moderately large problems. Using recent advances in sparsity-promoting techniques, we present a novel algorithm to solve this sparse sensor placement optimization for classification (SSPOC) that exploits low-dimensional structure exhibited by many high-dimensional systems. Our approach is inspired by compressed sensing, a framework that reconstructs data from few measurements. If only classification is required, reconstruction can be circumvented and the measurements needed are orders-of-magnitude fewer still. Our algorithm solves an 1 minimization to find the fewest nonzero entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space; these entries represent sensor locations that best inform the decision task. We demonstrate the SSPOC algorithm on five classification tasks, using datasets from a diverse set of examples, including physical dynamical systems, image recognition, and microarray cancer identification. Once training identifies sensor locations, data taken at these locations forms a low-dimensional measurement space, and we perform computationally efficient classification with accuracy approaching that of classification using full-state data. The algorithm also works when trained on heavily subsampled data, eliminating the need for unrealistic full-state training data.

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Section: Related Workmentioning

confidence: 99%

Sparse Sensor Placement Optimization for Classification

Brunton¹,

Brunton²,

Proctor³

et al. 2016

SIAM J. Appl. Math.

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“…We review the authentication algorithm based on Linear combination of templates computed as an l 2 -norm minimization problem [9]- [15], and then the 4th property is shown. …”

Section: Authentication Via L 2 -Norm Minimizationmentioning

confidence: 99%

“…In this study, the parameter p i is used for a templates set D or D i globally as well as the conventional schemes [9], [10], [14], [15], although the entropy of the parameter p i is reduced.…”

Section: B Authentication Algorithm With Protected Templatesmentioning

confidence: 99%

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Unitary Transform-Based Template Protection and Its Application to <i>l</i><sup>2</sup>-norm Minimization Problems

Nakamura

Tonomura

Kiya

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWe focus on the feature transform approach as one methodology for biometric template protection, where the template consists of the features extracted from the biometric trait. This study considers some properties of the unitary (including orthogonal) transform-based template protection in particular. It is known that the Euclidean distance between the templates protected by a unitary transform is the same as that between original (non-protected) ones as a property. In this study, moreover, it is shown that it provides the same results in l 2 -norm minimization problems as those of original templates. This means that there is no degradation of recognition performance in authentication systems using l 2 -norm minimization. Therefore, the protected templates can be reissued multiple times without original templates. In addition, a DFT-based template protection scheme is proposed as an unitary transform-based one. The proposed scheme enables to efficiently generate protected templates by the FFT, in addition to the useful properties. It is also applied to face recognition experiments to evaluate the effectiveness.

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“…Another important aspect for real life applications using sensitive biometric data is the provision of security and user privacy protection mechanisms, since the use of random features, instead of the actual biometric data for e.g. person identification, protects the original data [16] from malicious attacks.…”

Section: Introductionmentioning

confidence: 99%

Maximum Margin Projection Subspace Learning for Visual Data Analysis

Nikitidis

Tefas

Pitas

2014

IEEE Trans. on Image Process.

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Abstract-Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.

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Secure and Robust Iris Recognition Using Random Projections and Sparse Representations

Cited by 293 publications

References 47 publications

Sparse Sensor Placement Optimization for Classification

Sparse Sensor Placement Optimization for Classification

Unitary Transform-Based Template Protection and Its Application to <i>l</i><sup>2</sup>-norm Minimization Problems

Maximum Margin Projection Subspace Learning for Visual Data Analysis

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