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Information Forensics and Security, IEEE Transactions on

Issue 3  Part 2 • Date Sept. 2007

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Displaying Results 1 - 21 of 21
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Information Forensics and Security publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (34 KB)  
    Freely Available from IEEE
  • Guest Editorial: Special Issue on Human Detection and Recognition

    Page(s): 489 - 490
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    Freely Available from IEEE
  • Spoof Attacks on Gait Authentication System

    Page(s): 491 - 502
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2331 KB) |  | HTML iconHTML  

    Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system. View full abstract»

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  • Protecting Biometric Templates With Sketch: Theory and Practice

    Page(s): 503 - 512
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (706 KB) |  | HTML iconHTML  

    Secure storage of biometric templates has become an increasingly important issue in biometric authentication systems. We study how secure sketch, a recently proposed error-tolerant cryptographic primitive, can be applied to protect the templates. We identify several practical issues that are not addressed in the existing theoretical framework, and show the subtleties in evaluating the security of practical systems. We propose a general framework to design and analyze a secure sketch for biometric templates, and give a concrete construction for face biometrics as an example. We show that theoretical bounds have their limitations in practical schemes, and the exact security of the system often needs more careful investigations. We further discuss how to use secure sketch in the design of multifactor authentication systems that allow easy revocation of user credentials. View full abstract»

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  • 3-D Face Recognition Based on Warped Example Faces

    Page(s): 513 - 528
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2261 KB) |  | HTML iconHTML  

    In this paper, we describe a novel 3-D face recognition scheme for 3-D face recognition that can automatically identify faces from range images, and is insensitive to holes, facial expression, and hair. In our scheme, a number of carefully selected range images constitute a set of example faces, and another range image is chosen as a ldquogeneric face.rdquo The generic face is then warped to match each of the example faces in the least mean square sense. Each such warp is specified by a vector of displacement values. In feature extraction operation, when a target face image comes in, the generic face is warped to match it. The geometric transformation used in the warping is a linear combination of the example face warping vectors. The coefficients in the linear combination are adjusted to minimize the root mean square error. After the matching process is complete, the coefficients of the composite warp are used as features and passed to a Mahalanobis-distance-based classifier for face recognition. Our technique is tested on a data set containing more than 600 range images. Experimental results in the access-control scenario show the effectiveness of the extracted features. View full abstract»

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  • Multiscale Representation for 3-D Face Recognition

    Page(s): 529 - 536
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (935 KB) |  | HTML iconHTML  

    The eigenfaces algorithm has long been a mainstay in the field of face recognition due to the high dimensionality of face images. While providing minimal reconstruction error, the eigenface-based transform space de-emphasizes high-frequency information, effectively reducing the information available for classification. Methods such as linear discriminant analysis (also known as fisherfaces) allow the construction of subspaces which preserve the discriminatory information. In this article, multiscale techniques are used to partition the information contained in the frequency domain prior to dimensionality reduction. In this manner, it is possible to increase the information available for classification and, hence, increase the discriminative performance of both eigenfaces and fisherfaces techniques. Motivated by biological systems, Gabor filters are a natural choice for such a partitioning scheme. However, a comprehensive filter bank will dramatically increase the already high dimensionality of extracted features. In this article, a new method for intelligently reducing the dimensionality of Gabor features is presented. The face recognition grand challenge dataset of 3-D face images is used to examine the performance of Gabor filter banks for face recognition and to compare them against other multiscale partitioning methods such as the discrete wavelet transform and the discrete cosine transform. View full abstract»

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  • 3-D Face Recognition With the Geodesic Polar Representation

    Page(s): 537 - 547
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3559 KB) |  | HTML iconHTML  

    The performance of automatic 3-D face recognition can be significantly improved by coping with the nonrigidity of the facial surface. In this paper, we propose a geodesic polar parameterization of the face surface. With this parameterization, the intrinsic surface attributes do not change under isometric deformations and, therefore, the proposed representation is appropriate for expression-invariant 3-D face recognition. We also consider the special case of an open mouth that violates the isometry assumption and propose a modified geodesic polar parameterization that also leads to invariant representation. Based on this parameterization, 3-D face recognition is reduced to the classification of expression-compensated 2-D images that can be classified with state-of-the-art algorithms. Experimental results verify theoretical assumptions and demonstrate the benefits of the geodesic polar parameterization on 3-D face recognition. View full abstract»

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  • Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

    Page(s): 548 - 558
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2603 KB) |  | HTML iconHTML  

    A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers. View full abstract»

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  • Components and Their Topology for Robust Face Detection in the Presence of Partial Occlusions

    Page(s): 559 - 569
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1391 KB) |  | HTML iconHTML  

    This paper presents a novel approach for automatic and robust object detection. It utilizes a component-based approach that combines techniques from both statistical and structural pattern recognition domain. While the component detection relies on Haar-like features and an AdaBoost trained classifier cascade, the topology verification is based on graph matching techniques. The system was applied to face detection and the experiments show its outstanding performance in comparison to conventional face detection approaches. Especially in the presence of partial occlusions, uneven illumination, and out-of-plane rotations, it yields higher robustness. Furthermore, this paper provides a comprehensive review of recent approaches for object detection and gives an overview of available databases for face detection. View full abstract»

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  • Class-Specific Kernel-Discriminant Analysis for Face Verification

    Page(s): 570 - 587
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1885 KB) |  | HTML iconHTML  

    In this paper, novel nonlinear subspace methods for face verification are proposed. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher's linear discriminant analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions. As for the FLDA for N class problems (N is greater than two), the transformation is not person specific. In order to remedy these limitations of FLDA, exploit the individuality of human faces and take into consideration the fact that the distribution of facial images, under different viewpoints, illumination variations, and facial expression is highly complex and nonlinear, novel kernel-discriminant algorithms are proposed. The new methods are tested in the face verification problem using the XM2VTS, AR, ORL, Yale, and UMIST databases where it is verified that they outperform other commonly used kernel approaches such as kernel-PCA (KPCA), kernel direct discriminant analysis (KDDA), complete kernel Fisher's discriminant analysis (CKFDA), the two-class KDDA, CKFDA, and other two-class and multiclass variants of kernel-discriminant analysis based on Fisher's criterion. View full abstract»

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  • A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems

    Page(s): 588 - 595
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    The methods introduced so far regarding discriminant non-negative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. In order to remedy this limitation, a novel DNMF method is presented that uses projected gradients. The proposed algorithm employs some extra modifications that make the method more suitable for classification tasks. The usefulness of the proposed technique to frontal face verification and facial expression recognition problems is demonstrated. View full abstract»

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  • Biometric Identification Based on Frequency Analysis of Cardiac Sounds

    Page(s): 596 - 604
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (421 KB) |  | HTML iconHTML  

    The performance of traditional biometric identification systems is, as yet, unsatisfactory in certain applications. For this reason, other physiological or behavioral characteristics have recently been considered, using new electrical or physical signals linked to a person's vital signs. This paper examines the biometric characteristics of phonocardiogram (PCG) signals from cardiac auscultation. The idea is that PCG signals have specific individual characteristics that can be taken into consideration as a physiological sign used in a biometric system. More specifically, the paper proposes a preliminary study related to the identification of individuals via frequency analysis of cardiac sounds. The results, obtained using a database containing several heart sound recordings from 20 different people, confirm the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system. View full abstract»

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  • An Evaluation of Image Sampling and Compression for Human Iris Recognition

    Page(s): 605 - 612
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1585 KB) |  | HTML iconHTML  

    The resilience of identity verification systems to subsampling and compression of human iris images is investigated for three high-performance iris-matching algorithms. For evaluation, 2156 images from 308 irises from the extended Chinese Academy of Sciences Institute of Automation database were mapped into a rectangular format with 512 pixels circumferentially and 80 radially. For identity verification, the 48 rows that were closest to the pupil were taken and images were resized by subsampling their Fourier coefficients. Negligible degradation in verification is observed if at least 171 circumferential and 16 radial Fourier coefficients are preserved, which would correspond to sampling the polar image at 342 times 32 pixels. With JPEG2000 compression, improved matching performance is observed down to 0.3 b/pixel (bpp), attributed to noise reduction without a significant loss of texture. To ensure that the iris-matching algorithms studied are not degraded by image compression, it is recommended that normalized iris images should be exchanged at 512 times 80 pixel resolution, compressed by JPEG 2000 to 0.5 bpp. This achieves a smaller file size than the ANSI/INCITS 379-2004 iris image interchange format. View full abstract»

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  • Palmprint Classification Using Multiple Advanced Correlation Filters and Palm-Specific Segmentation

    Page(s): 613 - 622
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1696 KB) |  | HTML iconHTML  

    We propose a palmprint classification algorithm with the use of multiple correlation filters per class. Correlation filters are two-class classifiers that produce a sharp peak when filtering a sample of their class and a noisy output otherwise. For every class, we train the filters for a palm at different locations, where the palmprint region has a high degree of line content. With the use of a line detection procedure and a simple line energy measure, any region of the palm can be scored and the top-ranked regions are used to train the filters for each class. Using an enhanced palmprint segmentation algorithm, our proposed classifier achieves an average equal error rate of 1.12 times10-4% on a large database of 385 classes using multiple filters of size 64 times 64 pixels. The average false acceptance rate when the false rejection rate is zero is 2.25 times10-4%. View full abstract»

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  • Gait Recognition Using Compact Feature Extraction Transforms and Depth Information

    Page(s): 623 - 630
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (998 KB) |  | HTML iconHTML  

    This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme. View full abstract»

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  • 3-D Face Recognition Using Local Appearance-Based Models

    Page(s): 630 - 636
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1760 KB) |  | HTML iconHTML  

    In this paper, we present a local appearance-based approach for 3-D face recognition. In the proposed algorithm, we first register the 3-D point clouds to provide a dense correspondence between faces. Afterwards, we analyze two mapping techniques-the closest-point mapping and the ray-casting mapping, to construct depth images from the corresponding well-registered point clouds. The depth images that are obtained are then divided into local regions where the discrete cosine transformation is performed to extract local information. The local features are combined at the feature level for classification. Experimental results on the FRGC version 2.0 face database show that the proposed algorithm performs superior to the well-known face recognition algorithms. View full abstract»

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  • Face Verification Using Template Matching

    Page(s): 636 - 641
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1443 KB) |  | HTML iconHTML  

    Human faces are similar in structure with minor differences from person to person. These minor differences may average out while trying to synthesize the face image of a given person, or while building a model of face image in automatic face recognition. In this paper, we propose a template-matching approach for face verification, which neither synthesizes the face image nor builds a model of the face image. Template matching is performed using an edginess-based representation of the face image. The edginess-based representation of face images is computed using 1-D processing of images. An approach is proposed based on autoassociative neural network models to verify the identity of a person. The issues of pose and illumination in face verification are addressed. View full abstract»

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  • IEEE Transactions on Information Forensics and Security EDICS

    Page(s): 642
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    Freely Available from IEEE
  • IEEE Transactions on Information Forensics and Security Information for authors

    Page(s): 643 - 644
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    Freely Available from IEEE
  • IEEE Signal Processing Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (31 KB)  
    Freely Available from IEEE

Aims & Scope

The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Chung C. Jay Kuo
University of Southern California