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Journal ArticleDOI

Template-based gait authentication through Bayesian thresholding

03 Jan 2019-IEEE/CAA Journal of Automatica Sinica (IEEE)-Vol. 6, Iss: 1, pp 209-219

TL;DR: This article proposes a method that uses the posterior probability of a Bayes ʼ classifier in place of the Euclidean distance and demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclideans distance approach and the cosine distance.
Abstract: While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes ʼ classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art.
Topics: Euclidean distance (57%), Gait analysis (52%), Posterior probability (51%), Feature extraction (51%), Biometrics (50%)
Citations
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Journal ArticleDOI
20 Feb 2020
TL;DR: The obtained results will prove that both the horizontal and vertical directions of the velocities of both movements include information that can be used to identify individuals, and this information can be obtained with micro-Doppler radar systems.
Abstract: This letter presents a method for person identification based on sit-to-stand and stand-to-sit movements using micro-Doppler radar measurements and a convolutional neural network (CNN). Two 24-GHz micro-Doppler radar systems placed directly above or behind participants will be used to measure the sit-to-stand and stand-to-sit movements of 10 participants. Images of the micro-Doppler signatures will be generated by subjecting the signals received by the radar to short-time Fourier transform. The generated images will then be used as input for the CNNs for training and evaluation purposes. The experiments verified the ability of the method to accurately identify people by measuring both their sit-to-stand and stand-to-sit movements. The identification accuracies for the sit-to-stand and stand-to-sit measurements were 93.6% and 94.9%, respectively, using the data of the radar placed above the participant, whereas the accuracy when placing the radar behind the participant was 92.9% for the sit-to-stand and 93.9% for the stand-to-sit movements. The obtained results will prove that both the horizontal and vertical directions of the velocities of both movements include information that can be used to identify individuals, and this information can be obtained with micro-Doppler radar systems.

3 citations


Cites background from "Template-based gait authentication ..."

  • ...2975219 In many studies, the sensors used in authentication techniques based on the gait and other forms of behavior have been cameras [3], [4]....

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Posted Content
TL;DR: A novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach is introduced.
Abstract: Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing methods to extract information necessary to fully characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel end to-end deep learning framework that simultaneously predicts the identity, activity and the location of a user to create user profiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervention unlike systems that require user-initiated initialization, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performance of the system is evaluated through experiments.

2 citations


Cites background from "Template-based gait authentication ..."

  • ...Note that sub-carriers with indices [1, 2, 3, 4, 5, 6, 33, 60, 61, 62, 63, 64] have approximately zero amplitude in this case, and hence, highlighting the importance of the sparsity reduction proposed in Section III-B....

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  • ...1) User Authentication: Majority of the wireless aided user authentication systems in the literature require the user to carry or wear a device to facilitate the authentication process [6]– [8]....

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Journal ArticleDOI
TL;DR: The results suggest that combining sit-to-stand and stand- to-sit movements provides sufficient information for accurate person identification and such information can be remotely acquired using Doppler radar measurements.
Abstract: This article demonstrates the identification of 10 persons with 99% accuracy achieved by combining micro-Doppler signatures of sit-to-stand and stand-to-sit movements. Data from these movements are measured using two radars installed above and behind the person. Images of Doppler spectrograms generated using the measured data are combined and input to a convolutional neural network. Experimental results show the significantly better accuracy of the proposed method compared with conventional methods that do not perform data combination. The accuracy of identifying 10 participants having similar ages and physical features was 96–99%, despite the relatively small training set (number of training samples: 50–90 Doppler radar images per person). These results suggest that combining sit-to-stand and stand-to-sit movements provides sufficient information for accurate person identification and such information can be remotely acquired using Doppler radar measurements.

1 citations


Cites background from "Template-based gait authentication ..."

  • ...Again, however, gait authentication systems relying on cameras [10], [11] present privacy issues....

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References
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Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

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TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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32,374 citations


"Template-based gait authentication ..." refers methods in this paper

  • ...A template region selection technique called the genetic template segmentation (GTS) [12] used the genetic algorithm [13] to automatically locate masking regions....

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28 Jul 2013
Abstract: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

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Journal ArticleDOI
Z. Q. John Lu1Institutions (1)
TL;DR: This section will review those books whose content and level reflect the general editorial poltcy of Technometrics.
Abstract: This section will review those books whose content and level reflect the general editorial poltcy of Technometrics. Publishers should send books for review to Ejaz Ahmed, Depatment of Mathematics and Statistics, University of Windsor, Windsor, ON N9B 3P4 (techeditor@uwindsoxca). The opinions expressed in this section are those of the reviewers These opinions do not represent positions of the reviewer's organization and may not reflect those of the editors or the sponsoring societies. Listed prices reflect information provided by the publisher and may not be current The book purchase programs of the American Society for Quality can provide some of these books at reduced prices for members. For infbrmation, contact the American Society for Quality at 1-800-248-1946.

2,303 citations


"Template-based gait authentication ..." refers background in this paper

  • ...(For LDA, Σk = Σ ∀k, where Σ is the covariance matrix of the whole transformed set of gallery features [26]....

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Journal ArticleDOI
Ju Han1, Bir Bhanu1Institutions (1)
TL;DR: Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
Abstract: In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches

1,446 citations


"Template-based gait authentication ..." refers background or methods in this paper

  • ...Taking sub-regions of the GEI that are unaffected by the covariate factors can give a much more robust gait biometric performance....

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  • ...This effect is much more pronounced for GEI, GEnI and AEI than the GTS as the covariate-resilient feature set of GTS causes the ROC curves of NN and BT to almost overlap....

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  • ...This performance step-up is clearly observable in the templates of GEI, GEnI, and AEI....

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  • ...The GEI template applied with the GTS mask yields the gait feature representation that provides the optimal overall performance to date....

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  • ...This can be clearly noted from the ROC curves of GEI, GEnI, and AEI where the area under the curve for CD-NN is greater than that of EDNN....

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Performance
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20211
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20191