1,720,963 research outputs found
Biometric walk recognizer: Gait recognition by a single smartphone accelerometer
No full textThis 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
Mobiles and wearables: owner biometrics and authentication
Full text linkWe 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
No full textIn 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
Gait Recognition: the Wearable Solution
No full textTwo 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
Embedded accelerometer signal normalization for cross-device gait recognition
No full textThis 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
Signal Enhancement and Efficient DTW-based Comparison for Wearable Gait Recognition
No full textThe popularity of biometrics-based user identification has significantly increased over the last few years. User
identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled
setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work
proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone
accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based
gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach, named MV-FWDTW, on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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