Paper1 Spoof Attacks on Gait Authentication System

IOSR Journal of Engineering (IOSRJEN), 2019

The Gait biometric is a complementary method used for human identification system. Gait means "a way or pattern of walking". Biometric systems are increasing tremendously in our day-today life providing more accurate and reliable identification and verification for secured authentication. For unique human identity, numbers of solutions are invented. According to the Psychological studies, the gait ability for unique identification of a person can be recognized which can be measured by different analysis techniques by visual method including camera from a distance that captures different angles of gait. It is also used in different applications such as in surveillance for security purpose which deals with the existing technology. This paper represents how the Gait Analysis is used in the field of security that can also be used in visual creation. Other fields like medical and biometric identification dealing with betterment for humans can also be included.

7th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS-2015)

In this paper, we demonstrate that gait patterns of an individual captured through a smartphone accelerometer can be imitated with the support of a digital treadmill. Furthermore, we design an attack for a baseline gait based authentication system (GBAS) and rigorously test its performance over an eighteen user data-set. By employing only two imitators and using a simple digital treadmill with speed control functionality, the attack increases the average false acceptance rate (FAR) from 5.8% to 43.66% for random forest, the best performing classifier in our experiments. More specifically, the FAR of eleven out of eighteen users increased to 70% or more. Our results call for a revisit of the design of the GBAS to make them resilient to such attacks. Bibtex: @INPROCEEDINGS{TreadmillAttack, author={Kumar, Rajesh and Phoha, V.V. and Jain, Anshumali}, booktitle={Biometrics: Theory, Applications and Systems (BTAS), 2015 IEEE Seventh International Conference on}, title={Treadmill Assisted Imitation Attack on Gait-based Authentication Systems}, year={2015}, month={Sept}, pages={1-7}, keywords={Gait biometrics;Imitation attacks;Mimicry attacks; Smartphone authentication;Machine learning in biometrics}, url = {https://www.academia.edu/12949056/Treadmill_Assisted_Imitation_Attack_on_Gait-based_Authentication_Systems}, }

Human recognition is always been an important task for various scenarios like wide area monitoring, asset protection, security etc. The first generation biometrics such as face, iris, palm, and fingerprint are working well in these applications but they require subject willingness for the entire recognition process. Gait recognition is one of the second generation biometrics which does not require subject willingness. It is behavioral biometrics, can be captured from the distance and no-touching. Even a low quality video can be used for gait recognition so more and more researchers are interested in it. This paper gives a brief survey of this second generation biometric technologies i.e. gait biometrics.

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015

Similar to fingerprint and iris pattern, everyone's gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the user's gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subject's trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).

International Journal of Machine Learning and Computing, 2011

Today's commercially available biometric systems show good reliability. However, they generally lack user acceptance. In general, people favour systems with the least amount of interaction. Using gait as a biometric feature would lessen such problems since it requires no subject interaction other than walking by. Consequently, this would increase user acceptance. And since highly motivated users achieve higher recognition scores, it increases the overall recognition rate as well. The latest research on gait-based identification-identification by observation of a person's walking style provides evidence that such a system is realistic and is likely to be developed and used in the years to come. This article outlines the application of gait technologies for security and other purposes. Gait analysis and recognition can form the basis of unobtrusive technologies for the detection of individuals who represent a security threat or behave suspiciously.

2019

Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device is authenticating the user. Amongst the many solutions proposed, gait recognition has been suggested to provide a reliable yet nonintrusive authentication approach – enabling both security and usability. Whilst several studies exploring mobilebased gait recognition have taken place, studies have largely been preliminary, with various methodological restrictions that have limited the number of participants, samples and type of features. Furthermore, prior studies have relied upon evaluating the approach on a limited number of activities namely walking and running, and there is some concern over the capacity of the approach to correctly verify individuals when the nature of the signals across a wider range of activities is likely to be more variable. This paper has sought to overcome these weaknesses ...

arXiv (Cornell University), 2022

Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of detailed gait data is captured and processed. The introduction and rise of the Metaverse is an example of a potentially popular application scenario in which the gait of users is transferred onto digital avatars. As a first step towards developing effective anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to gait recognition. We first extract categories of features from the literature on human gait perception and then design experiments for each category to assess how much the information they contain contributes to recognition success. We evaluated the utility of gait perturbation by means of naturalness ratings in a user study. Our results show that gait anonymization will be challenging, as the data is highly redundant and inter-dependent.

Pattern Recognition Letters, 2015

Human gait, a soft biometric helps to recognize people by the manner, they walk. This paper presents gait image features based on the information set theory, henceforth these are called gait information image features. The information set stems from a fuzzy set with a view to represent the uncertainty in the information source values using the entropy function. The proposed gait information image (GII) is derived by applying the concept of information set on the frames in one gait cycle and two features named gait information image with energy feature (GII-EF) and gait information image with sigmoid feature (GII-SF) are extracted. Nearest neighbor (NN) classifier is applied to identify the gait. The proposed features are tested on Casia-B dataset, SOTON small database with variations in clothing and carrying conditions and on OU-ISIR Treadmill B database with large variation in clothing conditions. Moreover, experiments are carried out on OU-ISIR Treadmill A database with slight variation in the walking speeds to demonstrate the robustness of the proposed features.

2010

The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.

Gait recognition methods in the task of biometric human identification, 2023

This article focuses on defining the problem of solving the problem of human identification by means of gait recognition in biometric identification systems. In order to determine the prospects of using gait recognition methods for human identification, a generalized model of a biometric identification system was considered, the main modules of the system were identified and a brief description of each module was provided. Next, the basic requirements for human biometric features were identified, the main biometric features were considered, and the features of their use in biometric identification systems were determined. The issue of using gait as a biometric identifier was also considered. It has been determined that the use of human gait allows to get rid of two main obstacles in the construction of biometric identification systems: users are not required to provide personal biometric information in advance, and the system does not require specialized equipment. Also, the issue of multi-view gait recognition was considered. Multi-view gait recognition involves capturing gait data from different angles and using this data to improve recognition accuracy. This approach has shown great promise in challenging scenarios such as low lighting conditions. Next, we analyzed scientific works in the field of gait recognition. It was determined that gait recognition methods can be divided into template-based and non-template-based methods. Template-based methods are aimed at obtaining patterns of torso or leg movements, i.e. they usually focus on the dynamics of movement in space or on spatio-temporal methods. Non-template-based methods consider shape and its features as more relevant characteristics, i.e., human recognition are performed using measurements that reflect the shape of the person. Next, we consider the use of different datasets in the process of training and testing human gait recognition methods. The main datasets were identified and their characteristics and features were collected. We considered the presence of various characteristics in the datasets, as well as the means of representing information about human gait. The research has identified the main problems and challenges facing researchers in this area, as well as the main trends in the field of human gait recognition in biometric identification systems.