Fingerprint Pairs Research Articles

Fingerprint Matching Method Based on Ant Colony Algorithm

Based on previous research, this paper proposes a model and implementation of large-scale fingerprint image retrieval. In the retrieval, the C-means clustering method is used to quickly retrieve fingerprint images, and the shortest path algorithm is used to achieve effective matching fingerprints, and finally, the fingerprints with a higher matching rate are obtained, thereby realizing rapid retrieval and matching of fingerprint IDs. The matching fingerprint minutiae features are analyzed, a retrieval method that can be used for fast fingerprint retrieval is given, and the principle of the method is expounded. The algorithm is used to filter and remove the pseudo-feature points of all fingerprints and is used to calculate the similarity between all minutiae operators. Then the operator with the highest similarity is selected, and the corresponding minutiae pair is used as the registration point pair to complete the two minutiae registration transformation of the point set. And finally, the pairing relationship of minutiae points is established. After that, the time complexity and space complexity of the selected retrieval method is analyzed. All fingerprint pairs with the “identical” relationship provided in the data file are used as query images to verify the retrieval method in the fingerprint dataset. The shortcomings and deficiencies of this method are pointed out, and other ways to solve this problem are introduced.

The low prevalence effect in fingerprint comparison amongst forensic science trainees and novices.

The low prevalence effect is a phenomenon whereby target prevalence affects performance in visual search (e.g., baggage screening) and comparison (e.g., fingerprint examination) tasks, such that people more often fail to detect infrequent target stimuli. For example, when exposed to higher base-rates of ‘matching’ (i.e., from the same person) than ‘non-matching’ (i.e., from different people) fingerprint pairs, people more often misjudge ‘non-matching’ pairs as ‘matches’–an error that can falsely implicate an innocent person for a crime they did not commit. In this paper, we investigated whether forensic science training may mitigate the low prevalence effect in fingerprint comparison. Forensic science trainees (n = 111) and untrained novices (n = 114) judged 100 fingerprint pairs as ‘matches’ or ‘non-matches’ where the matching pair occurrence was either high (90%) or equal (50%). Some participants were also asked to use a novel feature-comparison strategy as a potential attenuation technique for the low prevalence effect. Regardless of strategy, both trainees and novices were susceptible to the effect, such that they more often misjudged non-matching pairs as matches when non-matches were rare. These results support the robust nature of the low prevalence effect in visual comparison and have important applied implications for forensic decision-making in the criminal justice system.

Circuit Activity Fingerprinting Using Electromagnetic Side-Channel Sensing and Digital Circuit Simulations

This paper introduces a novel circuit identification method based on “fingerprints” of periodic circuit activity that does not rely on any circuit-specific reference measurements. We capture these “fingerprints”, consisting of fifty harmonics of the circuit activity, using digital circuit simulations and near-field measurements of the EM backscattering side-channel. Utilizing a novel technique and algorithm, we augment our measurements, removing sources of noise and other irregularities not present in the simulation, in order to relate an unknown circuit measurement with a known circuit simulation. A matching threshold of less than 1 dB difference between the simulated and measured fingerprints is set, and the matching performance is evaluated across multiple hardware instances exhibiting a strong resistance to false positives. Using various match statistics, decisions on the circuit identity can be made based on the simulated and measured fingerprint pair with the best matching performance. The results show that we can identify fingerprints of digital circuits with up to 95% accuracy using the proposed method.

A universal Wi-Fi fingerprint localization method based on machine learning and sample differences

Wi-Fi technology has become an important candidate for localization due to its low cost and no need of additional installation. The Wi-Fi fingerprint-based positioning is widely used because of its ready hardware and acceptable accuracy, especially with the current fingerprint localization algorithms based on Machine Learning (ML) and Deep Learning (DL). However, there exists two challenges. Firstly, the traditional ML methods train a specific classification model for each scene; therefore, it is hard to deploy and manage it on the cloud. Secondly, it is difficult to train an effective multi-classification model by using a small number of fingerprint samples. To solve these two problems, a novel binary classification model based on the samples’ differences is proposed in this paper. We divide the raw fingerprint pairs into positive and negative samples based on each pair’s distance. New relative features (e.g., sort features) are introduced to replace the traditional pair features which use the Media Access Control (MAC) address and Received Signal Strength (RSS). Finally, the boosting algorithm is used to train the classification model. The UJIndoorLoc dataset including the data from three different buildings is used to evaluate our proposed method. The preliminary results show that the floor success detection rate of the proposed method can reach 99.54% (eXtreme Gradient Boosting, XGBoost) and 99.22% (Gradient Boosting Decision Tree, GBDT), and the positioning error can reach 3.460 m (XGBoost) and 4.022 m (GBDT). Another important advantage of the proposed algorithm is that the model trained by one building’s data can be well applied to another building, which shows strong generalizable ability.

The prevalence effect in fingerprint identification: Match and non‐match base‐rates impact misses and false alarms

AbstractThe prevalence effect is a phenomenon whereby target prevalence impacts performance in visual search (e.g., baggage screening) and visual comparison (e.g., face‐matching) tasks – people more often 'miss' infrequent target stimuli. The current study investigated prevalence effects in fingerprint identification – an important visual comparison task used in criminal investigations. Participants (N = 287) judged 100 fingerprint pairs where the prevalence of match trials was either 10% (low), 50% (equal), or 90% (high), and half received trial‐level feedback on their performance. As predicted, low match prevalence increased errors on match trials (i.e., misses), whereas high match prevalence errors on non‐match trials (i.e., false alarms) – but only when participants received feedback. These effects were largely driven by changes in bias (C), rather than sensitivity (d’). These results suggest that the combination of feedback and match prevalence can impact the types of errors that fingerprint examiners may make in practice.

Joint Estimation of Pose and Singular Points of Fingerprints

Fingerprint pose estimation is a challenging problem since the pose is not defined by salient anatomical features and fingerprint images usually suffer from noise and small area. In this article, we proposed a method for joint estimation of pose and singular points of fingerprints, with the expectation that the pose and singular points can improve each other. By virtue of that singular points can be located accurately, we hope to improve the accuracy of pose estimation. Meanwhile, the robustness of pose estimation can improve the anti-noise performance of singular point detection. To achieve this, we propose a multi-task deep neural network, which contains a feature extraction body and two estimation heads for singular point and pose respectively. The proposed network can deal with various types of fingerprints, including plain, rolled and latent fingerprints. Experiments on four databases (NIST SD4, SD14, SD27 and FVC2004 DB1A) show that (1) the estimated poses and detected singular points are close to manual annotations despite of different image qualities; (2) the estimated poses for mated fingerprint pairs are consistent; and (3) the proposed pose estimation method outperforms state-of-the-art methods while utilized as pose constraint for a fingerprint indexing algorithm.

Latent Fingerprint Registration via Matching Densely Sampled Points

Latent fingerprint matching is a very important but unsolved problem. As a key step of fingerprint matching, fingerprint registration has a great impact on the recognition performance. Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae, and hence will certainly fail when there are no sufficient number of extracted minutiae due to small fingerprint area or poor image quality. Minutiae extraction has become the bottleneck of latent fingerprint registration. In this paper, we propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints through a dense fingerprint patch alignment and matching procedure. Given a pair of fingerprints to match, we bypass the minutiae extraction step and take uniformly sampled points as key points. Then the proposed patch alignment and matching algorithm compares all pairs of sampling points and produces their similarities along with alignment parameters. Finally, a set of consistent correspondences are found by spectral clustering. Extensive experiments on NIST27 database and MOLF database show that the proposed method achieves the state-of-the-art registration performance, especially under challenging conditions. Code is made publicly available at: https://github.com/Gus233/Latent-Fingerprint-Registration.

Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network

Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern. To overcome the limitation of handcraft features, we propose to train an end-to-end network to directly output pixel-wise displacement field between two fingerprints. The proposed network includes a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. By applying displacement fields reliably estimated by tracing high quality fingerprint videos to challenging fingerprints, we synthesize a large number of training fingerprint pairs with ground truth displacement fields. In addition, based on the proposed registration algorithm, we propose a fingerprint mosaicking method based on optimal seam selection. Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous dense registration methods in accuracy and efficiency. Mosaicking experiment on FVC2004 DB1 demonstrates that the proposed algorithm produced higher quality fingerprints than other algorithms which also validates the performance of our registration algorithm.

Similarity Filtering with Multibit Trees for Record Linkage

Record linkage is the process of identifying pairs of records that refer to the same real-world object within or across data files. Basically, each record pair is compared with a similarity function and then classified in supposedly matching and non-matching pairs. However, if every possible record pair has to be compared, the resulting number of comparisons leads to infeasible running times for large data files. In such situations, blocking or indexing methods to reduce the comparison space are required. In this paper we propose a new blocking technique (Q-gram Fingerprinting) that efficiently filters record pairs according to an approximation of a q-gram similarity function. The new method first transforms data records into bit vectors, the fingerprints, and then filters pairs of fingerprints by use of a Multibit Tree according to a user-defined similarity threshold. We examined the effect of different parameter choices of Q-gram Fingerprinting, tested its scalability, and performed a comparison study including several alternative methods using simulated person data. The comparison study showed promising results for the proposed method.

RSS Indoor Localization Based on a Single Access Point.

This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor—WKNN and Minimum Mean Square Error—MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the “Tolerance Based—Normal Probability Distribution (TBNPD)” algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP.

A Study of Age and Ageing in Fingerprint Biometrics

Thanks to Mr. James Bond, we are aware that diamonds are forever but, are fingerprints? It is well known that biometrics brings to the security field a new paradigm; unlike traditional systems, individuals are not identified by something that they have or they know, but by what they are. While such an approach entails some clear advantages, an important question remains: is what we are today the same as what we will be tomorrow? This paper addresses such a key problem in the fingerprint modality based on a database of over 400K impressions coming from more than 250K different fingers. The database was acquired under real operational conditions and contains fingerprints from subjects aged 0–25 and 65–98 years. Fingerprint pairs were collected with a time difference that ranges between 0 and 7 years. Such a unique set of data has allowed us to analyze both the age and ageing effects, shedding some new light into issues, such as fingerprint permanence and fingerprint quality.

Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching

Nowadays, the majority of fingerprint quality, matching and feature extraction algorithms are developed and trained on fingerprints of adults. Accordingly, the processing of children’s fingerprints presents performance issues derived for the most part from: (1) their smaller size and finer ridge structure; (2) their higher variability over time due to the displacement of minutiae induced by growth. The present article is focused on the second factor. The rapid growth of children fingerprints causes a significant displacement of the minutiae points between samples of the same finger acquired with a few years distance from each other. This displacement results in a decrease of the accuracy of fingerprint recognition systems when the reference and probe sample drift apart in time. This effect is known as biometric ageing. In the present study we propose to address this issue by developing and validating a minutiae-based growth model, derived from a database of over 60,000 children’s fingerprints, acquired in real operational conditions, ranging between 5 and 16 years of age, with a time difference between fingerprint pairs re-enrolments of up to 6 years. We analyze two potential application scenarios for the developed growth model. On one hand, we use the model to grow children’s fingerprints in order to spread out the minutiae points to attain sizes similar to those of a sample captured at a later point in time. On the other hand, we apply the model to rejuvenate fingerprints enrolled at a later stage by contracting the minutiae points so that their location is more similar to those of a sample acquired earlier. In both scenarios, the application of the growth model to produce artificially grown/rejuvenated fingerprint minutiae templates results in a significant improvement of the matching scores compared to the ones produced by original fingerprints.

A biased opinion: Demonstration of cognitive bias on a fingerprint matching task through knowledge of DNA test results

One study is presented which explores the biasing effects of irrelevant contextual information on a fingerprint matching task. Bias was introduced by providing the outcomes of a DNA test relating to each fictitious case under consideration. This was engineered to suggest either a match, no match, or an inconclusive outcome, and was thus either consistent, misleading or unbiased depending on the ground truth of each fingerprint pair. The results suggested that, when the difficulty of the fingerprint matching task was measurably increased, participants became more vulnerable to the biasing information. Under such conditions, when performance was good, misleading evidence lowered accuracy, and when performance was weaker, consistent evidence improved accuracy. As such, the results confirmed existing demonstrations of cognitive bias from contextual information in the fingerprint task. Moreover, by taking a process-based approach, it became possible to articulate the concerns, and the potential solutions, at each stage of the workflow. The results offer value for the forensic science community in extending the evidence-base regarding cognitive bias, and in articulating routes to improve the credibility of fingerprint decisions.

Fingerprint indexing with pose constraint

Fingerprint indexing is critical for identifying fingerprints efficiently in large-scaled fingerprint databases. The state-of-the-art indexing accuracies are achieved by minutiae-based indexing approaches. A major difference of these approaches is how they deal with fingerprint registration problem. Some of the approaches do not use registration, while others perform relative/pairwise registration (e.g., based on mated minutiae between two fingerprints). However, the former approach is not accurate, since without geometric constraints, many false matches can be found even for non-mated fingerprints. The latter is not efficient for large databases since the relative registration step has to be performed for each pair of fingerprints. It is desirable to develop an absolute registration approach, which can register any single fingerprint into a common coordinate system, and therefore can address efficiency and accuracy problems simultaneously. In this paper, we proposed a fingerprint pose estimation algorithm which can register fingerprints into a common finger coordinate system. Fingerprint pose estimation problem is viewed as a two-class classification problem and approached by sliding window classifiers trained on labeled data. Ridge orientation information is used as features instead of singularities which are often affected by noise. With the pose estimation, we are able to refine the matched minutiae with global spatial constraint. By combining the proposed pose estimation algorithm with an improved Locality Sensitive Hashing algorithm for MCC descriptor, our indexing system outperformed previous state-of-the-art on widely used databases and its scalability was tested on a large database with one million fingerprints.

Putting bias into context: The role of familiarity in identification.

Previous demonstrations of context effects in the forensic comparison sciences have shown that the number of "match" responses a person makes can be swayed by case information. Less clear is whether these effects are a result of changes in accuracy (e.g., discrimination ability), a shift in response bias (e.g., tendency to say "match" or "no match") or a mix of the 2. We present a series of experiments where we use a signal detection framework to examine the effects of case information (separately) on forensic comparison accuracy and response bias. We also explore the role of familiarity as 1 potential mechanism for case information to sway accuracy. In Experiment 1, case information about crimes perceived to be more severe swayed people to say "match" more, but had little bearing on their ability to discriminate matching and nonmatching fingerprint pairs. In Experiment 2, case information did affect accuracy when it was familiar (i.e., if a previous similar case was associated with a "match" then people were more likely to also rate the current case as a "match," even though it was not). Even when we blinded people to all extrinsic case information in Experiment 3, accuracy was significantly affected by the familiarity of the fingerprints. These results demonstrate that contextual factors can have different (and independent) influences on accuracy and response bias and that even subtle information can affect accuracy if it is sufficiently similar to the case or trace at hand.

Coarse-Grained Architecture for Fingerprint Matching

Fingerprint matching is a key procedure in fingerprint identification applications. The minutiae-based fingerprint matching algorithm is one of the most typical algorithms achieving a reasonably correct recognition rate. This study proposes a coarse-grained parallel architecture called fingerprint matching core (FMC) to accelerate fingerprint matching. The proposed architecture has a two-level parallel structure (i.e., parallel among groups (PAG) and parallel in group (PIG)). A multirequest controller is added to the PAG structure to obtain a concurrent operation of the multiple processing element group (PEG). The DDR3 controller is used in the PIG structure to read eight minutiae from eight different fingerprints and realize the simultaneous computation of the eight PEs. The whole system is implemented on a Xilinx FPGA board with a Virtex VII XC7VX485T chip. The 16-PEG FMC achieves a throughput of about 9.63 million fingerprint pairs per second, which is larger than that achieved on a Tesla K20c platform. The software execution times are also measured on the 2.93GHz Intel Xeon 5670, 2.3GHz AMD Opteron(tm) Processor 6376, and Tesla K20c platforms. The Intel Xeon 5670 has two processors with 12 cores, and the AMD Opteron(tm) Processor 6376 has two processors with 16 cores. Moreover, the throughput is about 31 times that achieved on a 2.93GHz Intel Xeon 5670 single core.

Earthquake detection through computationally efficient similarity search.

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection-identification of seismic events in continuous data-is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact "fingerprints" of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

An Imperfect Match? Crime‐related Context Influences Fingerprint Decisions

SummaryResearch suggests that fingerprint interpretation is vulnerable to contextual influence. Specifically, as contextual information increases in emotional intensity, people appear to increase the rate at which they deem fingerprint pairs to share the same source. We investigated three alternative explanations for this finding: (i) that participants who complete multitrial experiments might make more matches over time; (ii) that the provision of any additional information could influence decision‐making; and (iii) that the effect could be limited to dichotomous response options. In Experiment 1, participants forced to make a match or non‐match decision on 96 fingerprint pairs made more matches when pairs were preceded by crime‐related photographs, relative to neutral photographs or no photographs. In Experiment 2, where an ‘unsure’ option was available, crime‐related context did not influence matches but decreased non‐matches. Further research is necessary to determine the relative contributions of emotionality and motivation to these effects. Copyright © 2015 John Wiley & Sons, Ltd.

ZIL: An Energy-Efficient Indoor Localization System Using ZigBee Radio to Detect WiFi Fingerprints

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

The nature of expertise in fingerprint matching: experts can do a lot with a little.

Expert decision making often seems impressive, even miraculous. People with genuine expertise in a particular domain can perform quickly and accurately, and with little information. In the series of experiments presented here, we manipulate the amount of “information” available to a group of experts whose job it is to identify the source of crime scene fingerprints. In Experiment 1, we reduced the amount of information available to experts by inverting fingerprint pairs and adding visual noise. There was no evidence for an inversion effect—experts were just as accurate for inverted prints as they were for upright prints—but expert performance with artificially noisy prints was impressive. In Experiment 2, we separated matching and nonmatching print pairs in time. Experts were conservative, but they were still able to discriminate pairs of fingerprints that were separated by five-seconds, even though the task was quite different from their everyday experience. In Experiment 3, we separated the print pairs further in time to test the long-term memory of experts compared to novices. Long-term recognition memory for experts and novices was the same, with both performing around chance. In Experiment 4, we presented pairs of fingerprints quickly to experts and novices in a matching task. Experts were more accurate than novices, particularly for similar nonmatching pairs, and experts were generally more accurate when they had more time. It is clear that experts can match prints accurately when there is reduced visual information, reduced opportunity for direct comparison, and reduced time to engage in deliberate reasoning. These findings suggest that non-analytic processing accounts for a substantial portion of the variance in expert fingerprint matching accuracy. Our conclusion is at odds with general wisdom in fingerprint identification practice and formal training, and at odds with the claims and explanations that are offered in court during expert testimony.