Mikko Lindholm

Identifying users of portable devices from gait pattern with accelerometers

Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7%) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10%, 18% and 19%, respectively.

Identifying people from gait pattern with accelerometers

Protecting portable devices is becoming more important, not only because of the value of the devices themselves, but for the value of the data in them and their capability for transactions, including m-commerce and m-banking. An unobtrusive and natural method for identifying the carrier of portable devices is presented. The method uses acceleration signals produced by sensors embedded in the portable device. When the user carries the device, the acceleration signal is compared with the stored template signal. The method consists of finding individual steps, normalizing and averaging them, aligning them with the template and computing cross-correlation, which is used as a measure of similarity. Equal Error Rate of 6.4% is achieved in tentative experiments with 36 test subjects.

Soft biometrics-combining body weight and fat measurements with fingerprint biometrics

The aim of this study was to examine whether using soft biometrics, i.e. easily measurable personal characteristics, such as weight and fat percentage, can improve the performance of biometrics in verification type applications. Fusing fingerprint biometrics with soft biometrics, in this case body weight measurements, decreased the total error rate (TER) from 3.9% to 1.5% in an experiment with 62 test subjects. This result shows that simple physiological measurements can be used to support biometric recognition. Furthermore, soft biometrics are unobtrusive, there is no risk of identity theft, the perception of the big-brother effect is small, the equipment needed is low-cost, and the methods are easy to understand. Soft biometrics alone are not suitable for security related applications, but they can be used for improving the performance of traditional biometrics. A potentially feasible use for soft biometrics may be found in non-security, convenience type cases, such as domestic applications.

Increasing Security of Mobile Devices by Decreasing User Effort in Verification

Reliable user verification is important for security of computers and personal devices; however, most of well-performing verification methods require explicit user effort. As a consequence, an access is granted for a long time after the only successful verification, which allows replacing the authorized user to the advantage of an impostor, as it is often the case with mobile phones. This work proposes a method of frequent user verification, based on cascading of unobtrusive biometrics with more reliable biometrics, provided explicitly, in such a way that explicit effort is required only if unobtrusive verification fails. Experiments with voice, gait and fingerprint data have shown that in most of noise conditions cascade was able to satisfy security requirements of False Accept Rate 1% and to achieve overall False Reject Rate 3% or less, while requiring explicit effort in 10 - 60% of cases.

Unobtrusive user identification with light biometrics

Biometric methods are used for recognition and verification of the identity of a person in many applications. Certain concerns over the obtrusive nature of their use, threats to privacy and even the danger of identity theft are rising. In this paper unobtrusive and privacy preserving light biometrics, such as height, weight, and body fat percentage are suggested for user identification. An experiment with 62 test subjects was conducted. In verification type of application total error rate of 11% was achieved using weight data alone and fusion with height data reduced the error rate to 2.4%. With a short list of five best scoring identities the percentage of cases with the correct identity on the list was 90% for weight alone and 100% for the combination of weight and height. The application domain for light biometrics is seen in non-security applications, such as homes, small offices and health clubs.

A REVIEW OF FINGERPRINT IMAGE ENHANCEMENT METHODS

Automatic fingerprint identification methods have become the most widely used technology in rapidly growing bioidentification applications. In this paper, different image enhancement approaches presented in the scientific literature are reviewed. Fingerprint verification can be divided into image acquisition, enhancement, feature extraction and matching steps. The enhancement step is needed to improve image quality prior to feature extraction. By far the most common approach relies on the filtering of the fingerprint images with filters adapted to local ridge orientation, but alternative approaches based on Fourier domain processing, direct ridge following and global features also exist. Methods of comparing the performance of enhancement methods are discussed. An example of the performance of different methods is given. Conclusions are made regarding the importance of effective enhancement, especially for noisy or low quality images.

Multi-sensor Logical Decision Making in the Single Location Surveillance Point System

As demand for surveillance of physical locations increases, automated decision making software can help maintain the rising costs of human monitoring. The variety in different types of sensors is also growing, and making use of their consolidated data can improve the decision making process. Using the constructive research method, we aim to define a design of a surveillance systems decision making component that utilizes data fusion from multiple types of sensors. As a solution we present the Logical Decision Making Server (LDMS), used in the Single Location Surveillance Point (SLSP), a system designed for monitoring an indoors location. The decision making capabilities in the LDMS are based on user-configurable security rules, which allow security personnel to define threats based on current and recent event reports from any or all of the environment’s sensors. The LDMS has been successfully developed and integrated into an SLSP implementation.

Human Balance Estimation using a Wireless 3D Acceleration Sensor Network

Balance and gait are a consequence of complex coordination between muscles, nerves, and central nervous system structures. The impairment of these functions can pose serious threats to independent living, especially in the elderly. This study was carried out to evaluate the performance of a wireless acceleration sensor network and its capability in balance estimation. The test has been carried out in eight patients and seven healthy controls. The Patients group had larger values in lateral amplitudes of the sensor displacement and smaller values in vertical displacement amplitudes of the sensor. The step time variations for the Patients were larger than those for the controls. A fuzzy logic and clustering classifiers were implemented, which gave promising results suggesting that a person with balance deficits can be recognized with this system. We conclude that a wireless system is easier to use than a wired one and more unobtrusive to the user

Wearable Context Aware Terminal for Maintenance Personnel

A wearable context aware terminal with net connection and spoken command input is presented. The context aware terminal can be used, for example, by janitors or other maintenance personnel for retrieving and logging information related to a location, such as a room. The main context cues used are user’s identity and location. The user is identified by biometrics which is also used for preventing unauthorized usage of the terminal and information accessible through the terminal. Location information is acquired by using signal strength information of existing Wireless Local Area Network (WLAN) infrastructure. Since the wearable terminal is envisaged to be used by maintenance personnel it was seen important to offer possibility to hands-free operation by using spoken commands. Tentative experiments show that this approach might be useful for maintenance personnel.

Shopper Behaviour Analysis Based on 3D Situation Awareness Information

The customer behaviour understanding is of major importance to brick and mortar retail struggling to keep their market share and competing with online retail. In this paper, we propose a customer behaviour tracking solution based on 3D data. We can cover large areas using numerous inexpensive networked 3D sensors for monitoring and tracking people and we have adopted an adaptive background model in order to be able to react to changes in the store environment. Experiments with people tracking and analysis of the trajectories in a department store show that use of inexpensive 3D sensors and lightweight computation allows classifying shopping behaviour into three classes (passers-by, decisive customers, exploratory customers) with 80 % accuracy.