The Experts below are selected from a list of 319365 Experts worldwide ranked by ideXlab platform
Anil K. Jain - One of the best experts on this subject based on the ideXlab platform.
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ICB - Fingerprint Synthesis: Evaluating Fingerprint Search at Scale
2018 International Conference on Biometrics (ICB), 2018Co-Authors: Kai Cao, Anil K. JainAbstract:A database of a large number of Fingerprint images is highly desired for designing and evaluating large scale Fingerprint search algorithms. Compared to collecting a large number of real Fingerprints, which is very costly in terms of time, effort and expense, and also involves stringent privacy issues, synthetic Fingerprints can be generated at low cost and does not have any privacy issues to deal with. However, it is essential to show that the characteristics and appearance of real and synthetic Fingerprint images are sufficiently similar. We propose a Generative Adversarial Network (GAN) to generate 512X512 rolled Fingerprint images. Our generative model for rolled Fingerprints is highly efficient (12ms/image) with characteristics of synthetic rolled prints close to real rolled images. Experimental results show that our model captures the properties of real rolled Fingerprints in terms of (i) Fingerprint image quality, (ii) distinctiveness and (iii) minutiae configuration. Our synthetic Fingerprint images are more realistic than other approaches.
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Fingerprint recognition of young children
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Anil K. Jain, Sunpreet S Arora, Kai Cao, Lacey Bestrowden, Anjoo BhatnagarAbstract:In 1899, Galton first captured ink-on-paper Fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify him ever after.” Since then, ink-on-paper Fingerprinting and manual comparison methods have been superseded by digital capture and automatic Fingerprint comparison techniques, but only a few feasibility studies on child Fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: 1) Do Fingerprints of young children possess the salient features required to uniquely recognize a child? 2) If so, at what age can a child’s Fingerprints be captured with sufficient fidelity for recognition? 3) Can a child’s Fingerprints be used to reliably recognize the child as he ages? For this paper, we collected Fingerprints of 309 children (0–5 years old) four different times over a one year period. We show, for the first time, that Fingerprints acquired from a child as young as 6-h old exhibit distinguishing features necessary for recognition, and that state-of-the-art Fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than six months. In addition, we use mixed-effects statistical models to study the persistence of child Fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, this paper demonstrates that Fingerprint recognition of young children (six months and older) is a viable solution based on available capture and recognition technology.
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longitudinal study of Fingerprint recognition
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Soweon Yoon, Anil K. JainAbstract:Human identification by Fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a Fingerprint pattern does not change over time (persistence). Although the uniqueness of Fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of Fingerprints, the persistence of Fingerprints has remained a general belief based on only a few case studies. In this study, Fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two Fingerprints in comparison, subject's age, and Fingerprint image quality. Longitudinal Fingerprint records of 15,597 subjects are sampled from an operational Fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of Fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two Fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and (ii) Fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of Fingerprint recognition accuracy becomes substantially large if either of the two Fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.
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orientation field estimation for latent Fingerprint enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jianjiang Feng, Jie Zhou, Anil K. JainAbstract:Identifying latent Fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked Fingerprints, the image quality of latent Fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked Fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in Fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel Fingerprint orientation field estimation algorithm based on prior knowledge of Fingerprint structure. We represent prior knowledge of Fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent Fingerprint database and an overlapped latent Fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.
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latent Fingerprint matching using descriptor based hough transform
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Alessandra A Paulino, Jianjiang Feng, Anil K. JainAbstract:Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is a routine procedure that is extremely important to forensics and law enforcement agencies. Latents are partial Fingerprints that are usually smudgy, with small area and containing large distortion. Due to these characteristics, latents have a significantly smaller number of minutiae points compared to full (rolled or plain) Fingerprints. The small number of minutiae and the noise characteristic of latents make it extremely difficult to automatically match latents to their mated full prints that are stored in law enforcement databases. Although a number of algorithms for matching full-to-full Fingerprints have been published in the literature, they do not perform well on the latent-to-full matching problem. Further, they often rely on features that are not easy to extract from poor quality latents. In this paper, we propose a new Fingerprint matching algorithm which is especially designed for matching latents. The proposed algorithm uses a robust alignment algorithm (descriptor-based Hough transform) to align Fingerprints and measures similarity between Fingerprints by considering both minutiae and orientation field information. To be consistent with the common practice in latent matching (i.e., only minutiae are marked by latent examiners), the orientation field is reconstructed from minutiae. Since the proposed algorithm relies only on manually marked minutiae, it can be easily used in law enforcement applications. Experimental results on two different latent databases (NIST SD27 and WVU latent databases) show that the proposed algorithm outperforms two well optimized commercial Fingerprint matchers. Further, a fusion of the proposed algorithm and commercial Fingerprint matchers leads to improved matching accuracy.
Jie Zhou - One of the best experts on this subject based on the ideXlab platform.
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CCBR - Fingerprint Presentation Attack Detection via Analyzing Fingerprint Pairs
Biometric Recognition, 2019Co-Authors: Meng Zhang, Jianjiang Feng, Jie ZhouAbstract:With the ever growing deployments of Fingerprint recognition systems, presentation attack detection has become the new bottleneck. In order to make full use of the difference in materials between the fake Fingerprint and the real Fingerprint, we proposed to utilize two images of a finger for classification. A pair of Fingerprints are first aligned using a deformable registration algorithm and then are fed into MobileNet-v2 networks to perform the classification. Experimental results on the public dataset LivDet 2011 show that the performance of the proposed approach is promising and prove the effectiveness of fusing two Fingerprints rather than using the Fingerprints separately.
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orientation field estimation for latent Fingerprint enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jianjiang Feng, Jie Zhou, Anil K. JainAbstract:Identifying latent Fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked Fingerprints, the image quality of latent Fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked Fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in Fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel Fingerprint orientation field estimation algorithm based on prior knowledge of Fingerprint structure. We represent prior knowledge of Fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent Fingerprint database and an overlapped latent Fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.
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CCBR - On the influence of Fingerprint area in partial Fingerprint recognition
Biometric Recognition, 2012Co-Authors: Fanglin Chen, Jie ZhouAbstract:Conventional algorithms for Fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial Fingerprints is still a challenge in Fingerprint matching. In Fingerprint recognition systems, there are frequently appeared partial Fingerprints, such as incompletely touching in Fingerprint scanning or latent Fingerprints. In this paper, we studied the influence of the Fingerprint area in partial Fingerprint recognition. First, a simulation scheme was proposed to construct a serial of partial Fingerprints with different area. Then, the influence of the Fingerprint area in partial Fingerprint recognition is studied. By comparing the performance of partial Fingerprint recognition with different Fingerprint area, some useful conclusions can be drawn: (1) The decrease of the Fingerprint area degrades the performance of partial Fingerprint recognition; (2) When the Fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase; (3) When the area of partial Fingerprints is smaller than 20,000 pixels (about fifth of the normal full Fingerprints), the performance of partial Fingerprint recognition becomes very poor; (4) The threshold value of a given false accept rate increases when the area of partial Fingerprints decrease a lot, but it remains almost the same if the area of partial Fingerprints decrease not so much, e.g., greater than 50,000 pixels (about half of the normal full Fingerprint). These observations can be helpful in improving the performance of partial Fingerprint recognition in the future.
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Separating Overlapped Fingerprints
IEEE Transactions on Information Forensics and Security, 2011Co-Authors: Fanglin Chen, Jianjiang Feng, Anil K. Jain, Jie Zhou, Jin ZhangAbstract:Fingerprint images generally contain either a single Fingerprint (e.g., rolled images) or a set of nonoverlapped Fingerprints (e.g., slap Fingerprints). However, there are situations where several Fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) Fingerprints are lifted from crime scenes or residue Fingerprints are left on Fingerprint sensors. Overlapped Fingerprints constitute a serious challenge to existing Fingerprint recognition algorithms, since these algorithms are designed under the assumption that Fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped Fingerprints into component or individual Fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped Fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize Fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped Fingerprints that leads to a significant improvement in the matching accuracy.
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BTAS - On separating overlapped Fingerprints
2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), 2010Co-Authors: Fanglin Chen, Jianjiang Feng, Jie ZhouAbstract:Fingerprint images generally either contain only a single Fingerprint or a set of non-overlapped Fingerprints (e.g., slap Fingerprints). However, there are situations where more than one Fingerprint overlap on each other. Such situations are frequently encountered when latent Fingerprints are lifted from crime scenes or residue Fingerprints are left on Fingerprint sensors. Overlapped Fingerprints constitute a serious challenge to existing Fingerprint recognition techniques, since these techniques are designed under the assumption that Fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped Fingerprints into component or individual Fingerprints. We first use local Fourier transform to estimate an initial overlapped orientation field, which contains at most two candidate orientations at each location. Then relaxation labeling technique is employed to label each candidate orientation as one of two classes. Based on the labeling result, we separate the initial overlapped orientation field into two orientation fields. Finally, the two Fingerprints are obtained by enhancing the overlapped Fingerprint using Gabor filters tuned to these two component separated orientation fields, respectively. Experimental results indicate that the algorithm leads to a good separation of overlapped Fingerprints.
Fanglin Chen - One of the best experts on this subject based on the ideXlab platform.
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CCBR - On the influence of Fingerprint area in partial Fingerprint recognition
Biometric Recognition, 2012Co-Authors: Fanglin Chen, Jie ZhouAbstract:Conventional algorithms for Fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial Fingerprints is still a challenge in Fingerprint matching. In Fingerprint recognition systems, there are frequently appeared partial Fingerprints, such as incompletely touching in Fingerprint scanning or latent Fingerprints. In this paper, we studied the influence of the Fingerprint area in partial Fingerprint recognition. First, a simulation scheme was proposed to construct a serial of partial Fingerprints with different area. Then, the influence of the Fingerprint area in partial Fingerprint recognition is studied. By comparing the performance of partial Fingerprint recognition with different Fingerprint area, some useful conclusions can be drawn: (1) The decrease of the Fingerprint area degrades the performance of partial Fingerprint recognition; (2) When the Fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase; (3) When the area of partial Fingerprints is smaller than 20,000 pixels (about fifth of the normal full Fingerprints), the performance of partial Fingerprint recognition becomes very poor; (4) The threshold value of a given false accept rate increases when the area of partial Fingerprints decrease a lot, but it remains almost the same if the area of partial Fingerprints decrease not so much, e.g., greater than 50,000 pixels (about half of the normal full Fingerprint). These observations can be helpful in improving the performance of partial Fingerprint recognition in the future.
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Separating Overlapped Fingerprints
IEEE Transactions on Information Forensics and Security, 2011Co-Authors: Fanglin Chen, Jianjiang Feng, Anil K. Jain, Jie Zhou, Jin ZhangAbstract:Fingerprint images generally contain either a single Fingerprint (e.g., rolled images) or a set of nonoverlapped Fingerprints (e.g., slap Fingerprints). However, there are situations where several Fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) Fingerprints are lifted from crime scenes or residue Fingerprints are left on Fingerprint sensors. Overlapped Fingerprints constitute a serious challenge to existing Fingerprint recognition algorithms, since these algorithms are designed under the assumption that Fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped Fingerprints into component or individual Fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped Fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize Fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped Fingerprints that leads to a significant improvement in the matching accuracy.
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BTAS - On separating overlapped Fingerprints
2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), 2010Co-Authors: Fanglin Chen, Jianjiang Feng, Jie ZhouAbstract:Fingerprint images generally either contain only a single Fingerprint or a set of non-overlapped Fingerprints (e.g., slap Fingerprints). However, there are situations where more than one Fingerprint overlap on each other. Such situations are frequently encountered when latent Fingerprints are lifted from crime scenes or residue Fingerprints are left on Fingerprint sensors. Overlapped Fingerprints constitute a serious challenge to existing Fingerprint recognition techniques, since these techniques are designed under the assumption that Fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped Fingerprints into component or individual Fingerprints. We first use local Fourier transform to estimate an initial overlapped orientation field, which contains at most two candidate orientations at each location. Then relaxation labeling technique is employed to label each candidate orientation as one of two classes. Based on the labeling result, we separate the initial overlapped orientation field into two orientation fields. Finally, the two Fingerprints are obtained by enhancing the overlapped Fingerprint using Gabor filters tuned to these two component separated orientation fields, respectively. Experimental results indicate that the algorithm leads to a good separation of overlapped Fingerprints.
Jianjiang Feng - One of the best experts on this subject based on the ideXlab platform.
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CCBR - Fingerprint Presentation Attack Detection via Analyzing Fingerprint Pairs
Biometric Recognition, 2019Co-Authors: Meng Zhang, Jianjiang Feng, Jie ZhouAbstract:With the ever growing deployments of Fingerprint recognition systems, presentation attack detection has become the new bottleneck. In order to make full use of the difference in materials between the fake Fingerprint and the real Fingerprint, we proposed to utilize two images of a finger for classification. A pair of Fingerprints are first aligned using a deformable registration algorithm and then are fed into MobileNet-v2 networks to perform the classification. Experimental results on the public dataset LivDet 2011 show that the performance of the proposed approach is promising and prove the effectiveness of fusing two Fingerprints rather than using the Fingerprints separately.
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orientation field estimation for latent Fingerprint enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jianjiang Feng, Jie Zhou, Anil K. JainAbstract:Identifying latent Fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked Fingerprints, the image quality of latent Fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked Fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in Fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel Fingerprint orientation field estimation algorithm based on prior knowledge of Fingerprint structure. We represent prior knowledge of Fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent Fingerprint database and an overlapped latent Fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.
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latent Fingerprint matching using descriptor based hough transform
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Alessandra A Paulino, Jianjiang Feng, Anil K. JainAbstract:Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is a routine procedure that is extremely important to forensics and law enforcement agencies. Latents are partial Fingerprints that are usually smudgy, with small area and containing large distortion. Due to these characteristics, latents have a significantly smaller number of minutiae points compared to full (rolled or plain) Fingerprints. The small number of minutiae and the noise characteristic of latents make it extremely difficult to automatically match latents to their mated full prints that are stored in law enforcement databases. Although a number of algorithms for matching full-to-full Fingerprints have been published in the literature, they do not perform well on the latent-to-full matching problem. Further, they often rely on features that are not easy to extract from poor quality latents. In this paper, we propose a new Fingerprint matching algorithm which is especially designed for matching latents. The proposed algorithm uses a robust alignment algorithm (descriptor-based Hough transform) to align Fingerprints and measures similarity between Fingerprints by considering both minutiae and orientation field information. To be consistent with the common practice in latent matching (i.e., only minutiae are marked by latent examiners), the orientation field is reconstructed from minutiae. Since the proposed algorithm relies only on manually marked minutiae, it can be easily used in law enforcement applications. Experimental results on two different latent databases (NIST SD27 and WVU latent databases) show that the proposed algorithm outperforms two well optimized commercial Fingerprint matchers. Further, a fusion of the proposed algorithm and commercial Fingerprint matchers leads to improved matching accuracy.
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Latent Fingerprint Matching
Pattern Analysis and Machine Intelligence IEEE Transactions on, 2011Co-Authors: Anil K. Jain, Jianjiang FengAbstract:Latent Fingerprint identification is of critical importance to law\nenforcement agencies in identifying suspects: Latent Fingerprints\nare inadvertent impressions left by fingers on surfaces of objects.\nWhile tremendous progress has been made in plain and rolled Fingerprint\nmatching, latent Fingerprint matching continues to be a difficult\nproblem. Poor quality of ridge impressions, small finger area, and\nlarge nonlinear distortion are the main difficulties in latent Fingerprint\nmatching compared to plain or rolled Fingerprint matching. We propose\na system for matching latent Fingerprints found at crime scenes to\nrolled Fingerprints enrolled in law enforcement databases. In addition\nto minutiae, we also use extended features, including singularity,\nridge quality map, ridge flow map, ridge wavelength map, and skeleton.\nWe tested our system by matching 258 latents in the NIST SD27 database\nagainst a background database of 29,257 rolled Fingerprints obtained\nby combining the NIST SD4, SD14, and SD27 databases. The minutiae-based\nbaseline rank-1 identification rate of 34.9 percent was improved\nto 74 percent when extended features were used. In order to evaluate\nthe relative importance of each extended feature, these features\nwere incrementally used in the order of their cost in marking by\nlatent experts. The experimental results indicate that singularity,\nridge quality map, and ridge flow map are the most effective features\nin improving the matching accuracy.
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Separating Overlapped Fingerprints
IEEE Transactions on Information Forensics and Security, 2011Co-Authors: Fanglin Chen, Jianjiang Feng, Anil K. Jain, Jie Zhou, Jin ZhangAbstract:Fingerprint images generally contain either a single Fingerprint (e.g., rolled images) or a set of nonoverlapped Fingerprints (e.g., slap Fingerprints). However, there are situations where several Fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) Fingerprints are lifted from crime scenes or residue Fingerprints are left on Fingerprint sensors. Overlapped Fingerprints constitute a serious challenge to existing Fingerprint recognition algorithms, since these algorithms are designed under the assumption that Fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped Fingerprints into component or individual Fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped Fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize Fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped Fingerprints that leads to a significant improvement in the matching accuracy.
Roksana Boreli - One of the best experts on this subject based on the ideXlab platform.
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Linking wireless devices using information contained in Wi-Fi probe requests
Pervasive and Mobile Computing, 2013Co-Authors: Mathieu Cunche, Mohamed Ali Kaafar, Roksana BoreliAbstract:Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi Fingerprint, i.e. the \SSIDs\ of their preferred wireless networks. The content of those Wi-Fi Fingerprints can reveal different types of information about the owner. We focus on the relation between the Fingerprints and the links between the owners. Our hypothesis is that social links between devices owners can be identified by exploiting the information contained in the Fingerprint. More specifically we propose to consider the similarity between Fingerprints as a metric, with the underlying idea: similar Fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss potential countermeasures and propose a new one based on geolocation. This study is based on a dataset collected in Sydney, Australia, composed of Fingerprints belonging to more than 8000 devices.
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I know who you will meet this evening! Linking wireless devices using Wi-Fi probe requests
2012Co-Authors: Mathieu Cunche, Mohamed Ali Kaafar, Roksana BoreliAbstract:Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi Fingerprint, i.e. the SSIDs of their preferred wireless networks. The content of those Wi-Fi Fingerprints can reveal different types of information about the owner. We focus on the relation between the Fingerprints and the links between the owners. Our hypothesis is that social links between devices owners can be identified by exploiting the information contained in the Fingerprint. More specifically we propose to consider the similarity between Fingerprints as a metric, with the underlying idea: similar Fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss how Wi-Fi Fingerprint can reveal informations on the nature of the links between users. This study is based on a dataset collected in Sydney, Australia, composed of Fingerprints corresponding to more than 8000 devices.
