Performance and security analysis of Gait-based user authentication

Verifying the identity of a user, usually referred to as user authentication, before granting access to the services or objects is a very important step in many applications. People pass through some sorts of authentication process in their daily life. For example, to prove having access to the computer the user is required to know a password. Similarly, to be able to activate a mobile phone the owner has to know its PIN code, etc. Some user authentication techniques are based on human physiological or behavioral characteristics such as fingerprints, face, iris and so on. Authentication methods differ in their advantages and disadvantages, e.g. PIN codes and passwords have to be remembered, eye-glasses must be taken off for face authentication, etc. Security and usability are important aspects of user authentication. The usability aspect relates to the unobtrusiveness, convenience and user-friendliness of the authentication technique. Security is related to the robustness of the authentication method against attacks. Recent advances in electronic chip development offer new opportunities for person authentication based on his gait (walking style) using small, light and cheap sensors. One of the primary advantages of this approach is that it enables unobtrusive user authentication. Although studies on human recognition based on gait indicate encouraging performances, the security per se (i.e. robustness and/or vulnerability) of gait-based recognition systems has received little or no attention. The overall goal of the work presented in this thesis is on performance and security analysis of gait-based user authentication. The nature of the contributions is not on developing novel algorithms, but rather on enhancing existing approaches in gait-based recognition using small and wearable sensors, and developing new knowledge on security and uniqueness of gait. The three main research questions addressed in this thesis are: (1) What are the performances of recognition methods that are based on the motion of particular body parts during gait? (2) How robust is the gait-based user authentication? (3) What aspects do influence the uniqueness of human gait? In respect to the first research question, the thesis identifies several locations on the body of the person, whose motion during gait can provide identity information. These body parts include hip, trouser pockets, arm and ankle. Analysis of acceleration signals indicates that movements of these body segments have some discriminative power. This might make these modalities suitable as an additional factor in multi-factor authentication. For the research question on security as far as we know, this thesis is the first extensive analysis of gait authentication security (in case of hip motion). A gait-based authentication system is studied under three attack scenarios. These attack scenarios include a minimal effort-mimicry (with restricted time and number of attempts), knowing the closest person in the database (in terms of gait similarity) and knowing the gender of the user in the database. The findings of the thesis reveal that the minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users in the database can be a threat to gait-based authentication systems. In the third research question, the thesis provides some insights towards understanding the uniqueness of gait in case of ankle/foot motion. In particular, it reveals the following: heavy footwear tends to diminish foot discriminativeness; a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward direction of the motion; and different parts of the gait cycle provide different level of discrimination. In addition, the thesis proposes taxonomy of user recognition methods based on gait. In addition, the thesis work has also resulted in the follwoing paper which is closely related or overlapping with papers mentioned below. Davrondzhon Gafurov, Kirsi Helkala and Torkjel Søndrol, Biometric Gait Authentication Using Accelerometer Sensor, Journal of Computers, 1(7), pp.51-59, 2006: http://www.academypublisher.com/jcp/vol01/no07/jcp01075159.pdf List of papers. The 8 research papers that constitute the main research part of the thesis are

Features Mapping Based Human Gait Recognition

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS. DOI: 10.17762/ijritcc2321-8169.15067

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available