Mohammad Omar Derawi

Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

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%.

Improved Cycle Detection for Accelerometer Based Gait Authentication

Over the last years, there has been an increasing research interest in the application of accelerometry data for many kinds of automated gait analysis algorithms. The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose an improved biometric gait recognition approach with a stable cycle detection mechanism and comparison algorithm. Unlike previous work on wearable gait recognition, which was based from simple average cycling methods to more complicated methods, this paper reports new techniques for which can improve the performance, by using simple approaches. Preprocessing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 60 volunteers and 12 sessions each volunteer and resulted in an equal error rate (EER) of 5.7%.

Gait and activity recognition using commercial phones

This paper presents the results of applying gait and activity recognition on a commercially available mobile smartphone, where both data collection and real-time analysis was done on the phone. The collected data was also transferred to a computer for further analysis and comparison of various distance metrics and machine learning techniques. In our experiment 5 users created each 3 templates on the phone, where the templates were related to different walking speeds. The system was tested for correct identification of the user or the walking activity with 20 new users and with the 5 enrolled users. The activities are recognised correctly with an accuracy of over 99%. For gait recognition the phone learned the individual features of the 5 enrolled participants at the various walk speeds, enabling the phone to afterwards identify the current user. The new Cross Dynamic Time Warping (DTW) Metric gives the best performance for gait recognition where users are identified correctly in 89.3% of the cases and the false positive probability is as low as 1.4%.

Fingerprint Recognition with Embedded Cameras on Mobile Phones

Mobile phones with a camera function are capable of capturing image and processing tasks. Fingerprint recognition has been used in many different applications where high security is required. A first step towards a novel biometric authentication approach applying cell phone cameras capturing fingerprint images as biometric traits is proposed. The proposed method is evaluated using 1320 fingerprint images from each embedded capturing device. Fingerprints are collected by a Nokia N95 and a HTC Desire. The overall results of this approach show a biometric performance with an Equal Error Rate (EER) of 4.5% by applying a commercial extractor/comparator and without any preproccesing on the images.

Scenario test of accelerometer-based biometric gait recognition

The goal of our research is to develop methods for accelerometer-based gait recognition, which are robust, stable and fast enough to be used for authentication on mobile devices. To show how far we are in reaching this goal we developed a new cycle extraction method, implemented an application for android phones and conducted a scenario test. We evaluated two different methods, which apply the same cycle extraction technique but use different comparison methods. 48 subjects took part in the scenario test. After enrolment they were walking for about 15 minutes on a predefined route. To get a realistic scenario this route included climbing of stairs, opening doors, walking around corners etc. About every 30 seconds the subject stopped and the authentication was started. This paper introduces the new cycle extraction method and shows the Detection Error Trade-Off-curves, error rates separated by route-section and subject as well as the computation times for enrolment and authentication on a Motorola milestone phone.

Towards an automatic gait recognition system using activity recognition (wearable based)

The need of increasing the security measures in mobile devices has led researchers focus on finding new security mechanisms. In this paper we propose a solution to keep data secured by ensuring that only the authorized user can access the data in a mobile phone. By using gait recognition as an important element for the authentication process we propose an automatic gait recognition system to be used for continuous authentication. Since recent gait recognition research only focuses on manual extraction of walking activities from the accelerometer signal, a solution to this issue could be activity recognition that would reduce the disadvantages of gait recognition by identifying the activities of a person continuously and automatically. Activity recognition would not only make it possible to authenticate the user in different daily activities like slow walking, normal walking, fast walking even running, but also help in avoiding authentication when the user is in passive state like sitting, standing still, etc.. This is one of the key factors and an interesting challenge which would benefit the data security area.

Fusion of gait and fingerprint for user authentication on mobile devices

A new multi-modal biometric authentication approach using gait signals and fingerprint images as biometric traits is proposed. The individual comparison scores derived from the gait and fingers are normalized using four methods (min-max, z-score, median absolute deviation, tangent hyperbolic) and then four fusion approaches (simple sum, user-weighting, maximum score and minimum core) are applied. Gait samples are obtained by using a dedicated accelerometer sensor attached to the hip. The proposed method is evaluated using 7200 fingerprint images and gait samples. Fingerprints are collected by a capacitive line sensor, an optical sensor with total internal reflection and a touch-less optical sensor. The fusion results of these two biometrics show an improved performance and a large step closer for user authentication on mobile devices.

Biometric access control using Near Field Communication and smart phones

Near Field Communication or NFC is a short-range communication channel that is one of the most promising technologies around. One of the purposes for this technology is to simplify first-time connections to other wireless technologies, like Wi-Fi and Bluetooth. In this article we will show how Near Field Communication in a Samsung Nexus S smartphone can be used as part of a two-factor access control system for unlocking a door. Biometric Fingerprint recognition is used for authentication and NFC will be used to transmit authentication information to computer controlling the door. We will define some requirements for the system to increase security and propose some solutions for implementation to improve protection of biometric assets.

Geometric-Aligned Cancelable Fingerprint Templates

A minutiae encryption algorithm based on geometric transformation of minutiae positions is proposed to generate cancelable fingerprint templates. A geometric transformation is used for alignment. A parameter-controlled minutiae encryption is performed within a local area to generate a cancelable minutiae template, and then all local encryption results are superimposed to form a protected template. Parameters to control the minutiae encryption are generated independent of the geometric-aligned minutiae, which ensures solid non-invertibility compared to those cancelable template generating algorithms with to-be-encrypted minutiae information as parameters.

Biometric options for mobile phone authentication

Businesses and consumers are making increasing use of mobile phones to access corporate data and networks, along with products and services that may demand authentication. As personal mobile devices become more popular the user has come to expect the full range of services from the mobile Internet, as limitations around screen size and interaction capabilities have disappeared.