Biometric walk recognizer. Research and results on wearable sensor-based gait recognition

Gait is a biometric trait that can allow user authentication, though being classified as a "soft" one due to a certain lack in permanence, and to sensibility to specific conditions. The earliest research relies on computer vision-based approaches, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, which are able to capture the dynamics of the walking pattern through simpler 1D signals, has spurred a different research line. This capture modality can avoid some problems related to computer vision-based techniques, but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques, make this biometrics attractive and suggest to continue the investigations in this field. The first Chapters of this thesis deal with an introduction to biometrics, and more specifically to gait trait. A comprehensive review of technologies, approaches and strategies exploited by gait recognition proposals in the state-of-the-art is also provided. After such introduction, the contributions of this work are presented in details. Summarizing, it improves preceding result achieved during my Master Degree in Computer Science course of Biometrics and extended in my following Master Degree Thesis. The research deals with different strategies, including preprocessing and recognition techniques, applied to the gait biometrics, in order to allow both an automatic recognition and an improvement of the system accuracy

Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

Biometric Walk Recognizer

In this paper we present a comparative test of different approaches to gait recognition by smartphone accelerometer. Our work provides a twofold contribution. The first one is related to the use of low-cost, built-in sensors that nowadays equip most mobile devices. The second one is related to the use of our system in identification mode. Instead of being used to just verify the identity of the device owner, it can also be used for identification among a set of enrolled subjects. Whether the identification is carried out remotely or even if its results are transmitted to a server, the system can also be exploited in a multibiometric setting. Its results can be fused with those from computer-vision based gait recognition, as well as other biometric modalities, to enforce identification for accessing critical locations/services. We obtained the best results by matching complete walk captures (Recognition Rate 0.95), but the implicit limitation is represented by the fixed number of steps in the walks. Therefore we also investigated methods based on first dividing the signal into steps. The best of these achieved a Recognition Rate of 0.88

Benefits of Gaussian Convolution in Gait Recognition

The first and still popular approach to gait recognition applies computer vision techniques to appearance-based features of walking patterns. More recently, wearable sensors have become attractive. The accelerometer is the most used one, being embedded in widespread mobile devices. Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a person from the walking pattern, and this stimulates to search for robust features, able to sufficiently characterize this trait. This paper presents some preliminary experiments using the convolution with Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public dataset, and achieve improved results compared with previous works exploiting the same dataset

Gait Recognition: the Wearable Solution

Two main factors encourage new investigations regarding biometric gait recognition. First, wearable sensors allow a new approach to this problem, which does not suffer from the hindering factors affecting computer vision methods. Occlusions, camera field of view/angle, or illumination are not issues anymore, and it is possible to better focus on gait intrinsic features. Second, wearable sensors are nowadays commonly embedded in widespread mobile devices, especially smartphones. This allows setting up a gait recognition system without special equipment (either cameras or equipped floors). However, even this new recognition approach suffers from specific limitations. Ground slope, shoe heels, walking speed, can cause signal distortions. Their possible effects must be investigated and addressed. The aim of this chapter is to provide the basics to approach gait recognition by mobile wearable sensors, and sketches the most promising techniques, while listing the (few) datasets available at present to test new algorithms

Biometric walk recognizer: Gait recognition by a single smartphone accelerometer

This paper presents an approach to gait recognition based on a single consumer accelerometer, built in most present mobile devices. It does not propose a completely novel algorithm, but rather investigates better ways to exploit the Dynamic TimeWarping (DTW), which is still one of the most used at present in literature. To this aim, the paper presents both a new segmentation algorithm to split the gait signal into cycles/steps, and investigates the best way to use the possibly segmented signal for recognition. Summarizing, the first contribution of the present work is the proposal of a new segmentation algorithm for the gait signal, which does not require any pre-processing, either interpolation or noise reduction, to enhance the original signal, and its comparison with two other state-of-the-art step segmentation algorithms. The second contribution is related to the extensive tests performed with the five different investigated matching methods. The tests are carried out exploiting all compared segmentation algorithms and three different datasets, collected using different sensors: the originally exploited BWR dataset, that includes walk templates from 30 volunteers, and two huge datasets used for this kind of testing, namely the ZJU-gaitacc and the OU-ISIR Inertial Sensor Database. Tests have been performed in both verification mode, either single-template or multiple-template, and identification mode, both closed and open set. The latter is rarely found in literature though representing the most frequently predictable applicative setting. It is worth underlining that the final goal is to allow using low-cost, built-in sensors that nowadays equip most smartphones. The best result in closed set identification, which is the identification mode usually reported in literature, is achieved using the most constrained method, i.e., limiting the walks in the gallery and in the probe to have a similar number of steps. It reaches ≈ 93 % of Recognition Rate (RR) on ZJUgaitacc dataset. The best result obtained with methods exploiting segmentation to overcome the mentioned limitation reaches ≈ 83 % of Recognition Rate (RR) on the same dataset, using our proposed algorithm. The best results in verification is achieved using multiple templates per user, again without segmentation, with an Equal Error Rate (EER) of 0.09, while the best results with segmentation is achieved again with our algorithm and is and EER of 0.10. This is a very good result for a soft biometrics as gait if often considered. As expected, open set identification achieves lower performance

Embedded accelerometer signal normalization for cross-device gait recognition

This paper proposes a ”soft” calibration of the signal from smartphone accelerometers, with the aim to improve crossdevice gait recognition. Other applications can also benefit from the same procedure. The procedure was evaluated on a dataset of walk signals collected by three different smartphones in two timeseparated sessions. The results are extremely satisfactory. For sake of space, only the most significant ones will be reported. In some recognition settings, especially cross-device ones, a relative improvement of over 100% of the starting performance was achieved

Feature-based analysis of gait signals for biometric recognition automatic extraction and Selection of features from accelerometer signals

Gait recognition has been traditionally tackled by computer vision techniques. As a matter of fact, this is a still very active research field. More recently, the spreading use of smart mobile devices with embedded sensors has also spurred the interest of the research community for alternative methods based on the gait dynamics captured by those sensors. In particular, signals from the accelerometer seem to be the most suited for recognizing the identity of the subject carrying the mobile device. Different approaches have been investigated to achieve a sufficient recognition ability. This paper proposes an automatic extraction of the most relevant features computed from the three raw accelerometer signals (one for each axis). It also presents the results of comparing this approach with a plain Dynamic Time Warping (DTW) matching. The latter is computationally more demanding, and this is to take into account when considering the resources of a mobile device. Moreover, though being a kind of basic approach, it is still used in literature due to the possibility to easily implement it even directly on mobile platforms, which are the new frontier of biometric recognition