Increasing Security of Mobile Devices by Decreasing User Effort in Verification

Vildjiounaite, Elena; Mäkelä, Satu-Marja; Lindholm, Mikko; Kyllönen, Vesa; Ailisto, Heikki

doi:10.1109/icsnc.2007.44

Cited by 18 publications

(13 citation statements)

References 8 publications

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“…In Vildjiounaite et al [17], experiments with voice, gait and fingerprint data have shown that in most cases FAR is around 0.01 and FRR is around 0.03. However, these results are the composite of three verification systems.…”

Section: Discussion Concerning the State-of-the-art Verification Stramentioning

confidence: 96%

“…While the first measurement concerns the robustness of the system to intruders, the second measurement regards usability and inconspicuousness of the system. In [17], a walking-based authentication system has been integrated with fingerprints data and voice recognition, ensuring in this way an high degree of reliability. Walking-based authentication provides 0.14 of equal error rate between FRR and FAR with little variations if the system is brought on the chest or on the waist.…”

Section: Introductionmentioning

confidence: 99%

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Personalization and user verification in wearable systems using biometric walking patterns

Casale

Pujol

Radeva

2011

Pers Ubiquit Comput

153

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In this article, a novel technique for user's authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a 'wild' scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.

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Section: Discussion Concerning the State-of-the-art Verification Stramentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Personalization and user verification in wearable systems using biometric walking patterns

Casale

Pujol

Radeva

2011

Pers Ubiquit Comput

153

View full text Add to dashboard Cite

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“…One of the actions taken in this step will be the reduction of noise in the collected data, for example by using a Moving Average or Weighted Moving Average f lter [5], but different types of f lters can also be applied [7]. In case of the MR100 sensor we also performed time equalization between the data points in a gait sample in this step, because the time between two data points was not constant.…”

Section: Average Cycle Methods (Acm)mentioning

confidence: 99%

“…In the ACM method the collected gait data is represented by an "average cycle" which is then used for comparisons in the analysis. Some of the reports are on performance of gait authentication [7], while others focus on security issues like how easy it is to copy the gait characteristics of another person [8]. Also issues related to different walking circumstances have been investigated before [5].…”

Section: Gait Recognitionmentioning

confidence: 99%

Eigensteps: A giant leap for gait recognition

Bours¹,

Shrestha²

2010

2010 2nd International Workshop on Security and Communication Networks (IWSCN)

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Abstract-In this paper we will show that using Principle Component Analysis (PCA) on accelerometer based gait data will give a large improvement on the performance. On a dataset of 720 gait samples (60 volunteers and 12 gait samples per volunteer) we achieved an EER of 1.6% while the best result so far, using the Average Cycle Method (ACM), gave a result of nearly 6%. This tremendous increase makes gait recognition a viable method in commercial applications in the near future.

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“…At score level, the fusion system normalizes the scores coming from each biometric modality matcher and combine them into a global score [145,93,73,86]. In the decision level approach the decisions about the user identity obtained from each trait separately are combined to obtain a final decision [184].…”

Section: Introductionmentioning

confidence: 99%

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

Sánchez¹

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In this thesis different techniques of image processing, machine learning and information fusion have been analysed in relation to their applicability to contact-less hand biometrics. To this end, a modular and configurable system that explodes the multimodal nature of the hand to increase its robustness and accuracy have been designed, implemented and evaluated. Given the fact that different applications have different accuracy and time performance needs, the evaluation is aimed to provide a fair comparative of methods under different environmental conditions that helps to adapt the system to the specific requirements of a concrete final application.A correct hand segmentation is necessary to extract reliable and invariant biometric features. For this reason, a comparative of different segmentation methods that include well-known methods such as global thresholding and graph cuts as well as a novelty flooding-based method which combines different image-based segmentation approaches. These methods have been compared using diverse datasets of images which cover a wide spectrum of capturing conditions.On the other hand, a comprehensive evaluation of different palmprint feature extraction methods comprising Gabor and Sobel filters, Local Binary Patterns, Local Derivative Patterns and Curvelets has been carried out. Different parameter configurations have also been tested with the aim of finding out which arrangement provides better results for each method. In addition to palmprint, also hand geometry features have been extracted. This evaluation includes also two different feature matching approaches: distance-based and Support Vector Machines.In addition, it has also been evaluated the feasibility of combining different feature extraction methods to yield into a more precise and robust multimodal solution. Two different levels for fusing the biometric information have been compared: score-level and feature-level.Finally, an evaluation methodology that allows for a fair comparison between different methods has been proposed. In particular, an evaluation protocol is offered with the aim of not only obtaining an extensive evaluation of the complete system under different environmental conditions, and testing multiple combinations of methods for each module, but also providing a basis against which to compare future research.Keywords: Accuracy, Biometrics, Computation Requirements, Configurable, Curvelets, Distance, Evaluation, Feature-level fusion, Flooding, Fusion, Gabor, Graph-Cuts, Hand Geometry, Local Binary Patterns, Local Derivative Patterns, Modular, Multimodal, Palmprint, Score-level fusion, Segmentation, Sobel, Support Vector Machines, Thresholding, Varied Environmental Conditions. R E S U M E NEn esta tesis se han analizado diferentes técnicas de procesado de imagen, aprendizaje automático y fusión de la información en relación a su aplicabilidad a la biometría de mano sin contacto. Para ello se ha diseñado, implementado y evaluado un sistema modular y configurable que explota la naturaleza multimodal de la...

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Increasing Security of Mobile Devices by Decreasing User Effort in Verification

Cited by 18 publications

References 8 publications

Personalization and user verification in wearable systems using biometric walking patterns

Personalization and user verification in wearable systems using biometric walking patterns

Eigensteps: A giant leap for gait recognition

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

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