Fingerprint Collection Research Articles

Preliminary analysis of latent fingerprints recovered from underneath bloodstains using matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI FT-ICR MSI)

Fingerprints and bloodstained evidence contain information critical to a forensic investigation as they both offer the potential for individual identification through comparison of friction ridge details or DNA profiling, respectively. Despite the strong evidentiary value of both types of exhibits, no method currently exists for the collection of fingerprints deposited underneath bloodstains without destruction of the fingerprint. This study evaluates a novel fingerprint recovery method using high-resolution mass spectrometry profiling and imaging. Latent fingerprints were recovered from underneath bloodstains by gently washing the bloodied surfaces with a dilute solution of anti-coagulant, and Matrix-Assisted Laser Desorption/Ionization Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (MALDI FT-ICR MS) was then used to compare the compositional variation of latent and chemically washed fingerprints. In profiling mode, 55% of residues detected in latent fingerprints were preserved after chemical washing, with greater detection frequency of sebaceous secretions. Conversely, mass spectrometry imaging analysis showed better representation of eccrine residues, where compounds such as phenol were found to increase in intensity by approximately 20% after chemical washing. Traditional fingerprint development powders were also used on recovered fingerprints to demonstrate the compatibility of the method with current forensic practice. The results of this study indicate the success of the fingerprint recovery method, highlighting its potential for use in future forensic casework to increase the evidentiary value of seized bloodied objects.

Informative Path Planning for Location Fingerprint Collection

Fingerprint-based indoor localization methods are promising due to the high availability of deployed access points and compatibility with commercial-off-the-shelf user devices. However, to train regression models for localization, an extensive site survey is required to collect fingerprint data from the target areas. In this paper, we consider the problem of informative path planning (IPP) to find the optimal walk for site survey subject to a budget constraint. IPP for location fingerprint collection is related to the well-known orienteering problem (OP) but is more challenging due to edge-based non-additive rewards and revisits. Given the NP-hardness of IPP, we propose two heuristic approaches: a Greedy algorithm and a genetic algorithm. We show through experimental data collected from two indoor environments with different characteristics that the two algorithms have low computation complexity, can generally achieve higher utility and lower localization errors compared to the extension of two state-of-the-art approaches to OP.

A study on Prevent fingerprints Collection in High resolution Image

A study on Prevent fingerprints Collection in High resolution Image

Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection

Mobile crowdsourcing (MCS) has shown great potential in received signal strength (RSS) fingerprint collection, in which an incentive mechanism plays a critical role to motivate users’ participation. However, how to quantify the quality of the gathered fingerprint data is still not addressed well in the design of incentive mechanism for MCS-based fingerprint collection. In this paper, a sensing quality metric is proposed to characterize the joint impact of users’ privacy protection and the spatial coverage of the submitted data. Given a limited budget, a basic incentive mechanism is devised to recruit appropriate users to maximize sensing quality. Considering that the cost of each user is regarded as private information and users may be attempted to misreport their costs to increase the revenue. Hence, an auction-based incentive mechanism is proposed to achieve the truthfulness of users’ costs, which is truthful, individually rational, computationally efficient and budget feasible. Simulation results show that our proposed schemes outperform the baseline schemes and the experiment with real-world data is carried out to evaluate the performance of our proposed basic incentive mechanism.

An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

The Received Signal Strength (RSS) fingerprint-based indoor localization method in Wireless Local Area Network (WLAN) environment has been widely used due to its low hardware requirements and high positioning accuracy. However, the positioning system requires enormous effort to collect fingerprint at each reference point in positioning area, which is time-consuming and laborious. For the practicability of the method, it is essential to reduce the workload of fingerprint collection in the offline phase and construct a fingerprint database efficiently and accurately. Utilizing the high spatial correlation of RSS, in this paper, an efficient fingerprint database construction method is proposed based on low-rank matrix completion. In the meanwhile, different from the traditional matrix completion model, we propose an improved optimization model by converting to the weighted nuclear norm and introducing l 1 -norm and Frobenius norm to recover unknown data accurately and reduce the influence of outliers and Gaussian noise simultaneously. Furthermore, we combine the K-Nearest Neighbor (KNN) method to solve the problem that matrix completion cannot deal with all unknown rows or columns in the matrix. The experiment results indicate that the proposed algorithm can recover the fingerprint database completely through a few RSS information and has higher accuracy than other algorithms in various scenarios.

Walrasian Equilibrium-Based Incentive Scheme for Mobile Crowdsourcing Fingerprint Localization.

Mobile crowdsourcing has been exploited to collect enough fingerprints for fingerprinting-based localization. Since the construction of a fingerprint database is time consuming, mobile users should be well motivated to participate in fingerprint collection task. To this end, a Walrasian equilibrium-based incentive mechanism is proposed in this paper to motivate mobile users. The proposed mechanism can eliminate the monopoly of the crowdsourcer, balance the supply and demand of fingerprint data, and maximize the benefit of all participators. In order to reach the Walrasian equilibrium, firstly, the social welfare maximization problem is constructed. To solve the original optimization problem, a dual decomposition method is employed. The maximization of social welfare is decomposed into the triple benefit optimization among the crowdsourcer, mobile users, and the whole system. Accordingly, a distributed iterative algorithm is designed. Through the simulation, the performance of the proposed incentive scheme is verified and analyzed. Simulation results demonstrated that the proposed iterative algorithm satisfies the convergence and optimality. Moreover, the self-reconstruction ability of the proposed incentive scheme was also verified, indicating that the system has strong robustness and scalability.

Towards smartphone-based touchless fingerprint recognition

The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.

An efficient crowd-sourcing-based approach for fingerprint database updating

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

Infrastructure-Free Indoor Pedestrian Tracking with Smartphone Acoustic-Based Enhancement.

Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by “enhancer” based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an“obstacle” into an “enhancer.” Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.

A SOCP-Based Automatic Visual Fingerprinting Method for Indoor Localization System

With the development of fingerprinting-based visual localization technology, a problem with this method is standing out, which is it takes great expenses in fingerprint collection. Recently, a few studies proposed some methods to alleviate this problem. However, the accuracy of the existing method is relatively low under some scenarios such as wide field of vision. In this paper, we propose a novel automatic visual fingerprinting (AVF) method for an indoor visual localization system. We consider the performance of AVF greatly hinges on visual odometry (VO) and ego-motion estimation (EME) block, which are two different ways of estimating fingerprint coordinates. Since both visual odometry and ego-motion estimation model are inaccurate, we build the least square model by second-order cone programming (SOCP). Our SOCP-based method is proposed to deal with the serious cumulative error and the random error introduced by VO and EME model, respectively. The purpose of this paper is improving the accuracy of the database generated by the AVF method under wide field of vision scenarios. Although the time costs are relatively higher than our compared method, fortunately, it is only on the off-line stage. The simulation results show that our method can provide a reliable image-location database with the consumer-grade smartphone camera.

Key technology research for mobile police terminal fingerprint collection for quick comparison using automated fingerprint identification system

When the face of the inspected person and the photograph on their identification (ID) card cannot be clearly matched, the individual is undocumented, or the ID is forged, it is often difficult for the on-site police to respond in time. This study proposes a number of key technologies for collecting fingerprints at mobile terminals for fast comparison using an automated fingerprint ID system (AFIS). These technologies ensure intelligent mobile fingerprint collection and allow the transmission of fingerprint information from the terminal to AFIS, over a wireless public security network for real-time fingerprint comparison. This study also analyzes the feasibility and effectiveness of the proposed technologies for system design and the applicability of fingerprint ID algorithms. The system achieved good results in a test by the Shanghai Public Security Bureau Criminal Investigation Corps.

A Novel Hybrid FLANN-PsO Technique for real Time Fingerprint Classification

In this paper we are presenting a Particle swarm optimized functional link neural network for classifying a collection of real time fingerprints in the field of biometric recognition. From the collected fingerprints the feature vectors are extracted as a collection of different angle oriented features using the Gabor filter bank. The classes of the fingerprints are assigned as per the Henry System. For classification a novel FLANN-PSO algorithm is used and tested for accuracy through different parameters and different angular features of the fingerprints. In this work we have obtained an accuracy of 98% for real time collected fingerprint images. It has been compared with other classifiers and the results obtained of this work in terms of accuracy and MSE value has shown appreciable improvement over the other algorithms.

Unsupervised Crowd-Assisted Learning Enabling Location-Aware Facilities

The accelerated evolution of Internet of Things (IoT) architectures and their incorporation in vehicles, buildings, or cities provide the ideal environment for the development and optimization of smart services. Under this light, positioning services that harvest location fingerprinting based on received signal strength indications (RSSIs) are widely popular due to the massive data generation that IoT settings provide. However, the labor-intensive and repetitive task of the radio map construction through offline RSSI fingerprint collection prevents such services from becoming standard equipment for future smart facilities. In this paper, we present a location-aware infrastructure that combines a broad sensing layer, edge computing, and centralized cloud federation support. Our setting gives rise to a sensing mechanism that enables in-facility crowdsourcing able to aid fingerprinting localization services. To that end, instead of extensive offline measurements, we use the facility occupants to gather unlabeled RSSI samples. To support the localization functionality, we develop a probabilistic cell-based model that is constructed by an unsupervised learning algorithm. Our black-box approach maintains the positioning accuracy regardless of changes in the underlying hardware or indoor environment. To evaluate our approach, we have deployed a multistorey facility testbed and performed an extensive real-subject trial to gather the unlabeled fingerprint dataset. The proposed unsupervised method yields average location classification accuracy of 0.8 that can rise up to 0.9 when a semi-supervised approach is considered. We also provide insights into the performance of the proposed infrastructure regarding mobility tracking, and under varying deployment scenarios.

Using Generative Adversarial Nets to Reduce Fingerprint Collection for Indoor Localization

Using Generative Adversarial Nets to Reduce Fingerprint Collection for Indoor Localization

Recognition of Latent Fingerprints and Ink-Free Printing Derived from Interfacial Segregation of Carbon Dots.

Carbon dots (CDs) have attracted increasing interest in recent years owing to their desirable properties. Despite the availability of diverse elaborate CDs, the function and application of CDs are far to be fully exploited. Here, biomass-derived carbon dots dispersed in a polymer matrix are found to behave as ink-free patterned substrates, which are demonstrated to be useful for nondestructive collection and recognition of latent fingerprints (LFPs), as well as printing. The coating of CD/poly(vinyl alcohol) solution on a LFP yields a flexible transparent film; a stable fluorescent fingerprint with clear ridge details enabling personal identification is formed on this film. Encouragingly, this method can be applied to nondestructively lift and recognize long-timely exposed LFPs from various surfaces. The mechanism for LFP collection and visualization is proposed, which should be ascribed to the interfacial segregation of CDs in the polymer matrix during the film forming process. This mechanism is further validated by and utilized for application of CD/polymer composites in relief printing, intaglio printing, and micro-trace transferring.

Two Novel Methods for Enhancing Postmortem Fingerprint Recovery from Mummified Remains.

Postmortem fingerprint collection is a routine part of many forensic death investigations. Although the production of postmortem prints is usually straight forward, several obstacles and scenarios can make the collection difficult. A common challenge occurs when finger pads are mummified. Several current techniques allow for softening and rehydration of mummified finger pads; however, despite the employment of such techniques, the production of adequate postmortem fingerprints can remain elusive. The authors present two techniques that can improve the chances of obtaining suitable fingerprints from mummified remains. The "baby powder method" involves applying a cornstarch-based powder, such as baby powder, onto the darkened and mummified finger pads, to allow better visualization of the fingerprint detail. The "transillumination method" involves carefully dissecting away the tissues underlying the finger pad, followed by placement of a bright light source underneath the finger pad, such that the finger ridge pattern is illuminated.

An Energy-Efficient Crowd-Sourcing-Based Indoor Automatic Localization System

The development of crowd sourcing and inertial sensors built into mobile devices provides a new opportunity for reducing the consumption of fingerprint-based indoor location. However, this development also brings a variety of new theoretical and practical problems. In this paper, we present an automatic room-level localization approach to resolve three key problems: the collection of fingerprints, determination of fingerprints’ location information, and energy consumption. Unlike previous works, we propose an active sampling algorithm to filter reliable and useful information from numerous users’ inputs. We also design a virtual room generation mechanism and floor plan mapping algorithm to construct a reliable room fingerprint database without manual annotations. Experimental results demonstrate that compared with the traditional algorithms the new approach supports room-level localization with higher than 95% accuracy and reduces energy consumption by more than 40%.

Microfluidic chip for non-invasive analysis of tumor cells interaction with anti-cancer drug doxorubicin by AFM and Raman spectroscopy.

Raman spectroscopy has been playing an increasingly significant role for cell classification. Here, we introduce a novel microfluidic chip for non-invasive Raman cell natural fingerprint collection. Traditional Raman spectroscopy measurement of the cells grown in a Polydimethylsiloxane (PDMS) based microfluidic device suffers from the background noise from the substrate materials of PDMS when intended to apply as an in vitro cell assay. To overcome this disadvantage, the current device is designed with a middle layer of PDMS layer sandwiched by two MgF2 slides which minimize the PDMS background signal in Raman measurement. Three cancer cell lines, including a human lung cancer cell A549, and human breast cancer cell lines MDA-MB-231 and MDA-MB-231/BRMS1, were cultured in this microdevice separately for a period of three days to evaluate the biocompatibility of the microfluidic system. In addition, atomic force microscopy (AFM) was used to measure the Young's modulus and adhesion force of cancer cells at single cell level. The AFM results indicated that our microchannel environment did not seem to alter the cell biomechanical properties. The biochemical responses of cancer cells exposed to anti-cancer drug doxorubicin (DOX) up to 24 h were assessed by Raman spectroscopy. Principal component analysis over the Raman spectra indicated that cancer cells untreated and treated with DOX can be distinguished. This PDMS microfluidic device offers a non-invasive and reusable tool for in vitro Raman measurement of living cells, and can be potentially applied for anti-cancer drug screening.

SAP: A Novel Stationary Peers Assisted Indoor Positioning System

Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationary peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs’ locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs’ locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.

Implementation of Wi-Fi Signal Sampling on an Android Smartphone for Indoor Positioning Systems.

Collecting and maintaining radio fingerprint for wireless indoor positioning systems involves considerable time and labor. We have proposed the quick radio fingerprint collection (QRFC) algorithm which employed the built-in accelerometer of Android smartphones to implement step detection in order to assist in collecting radio fingerprints. In the present study, we divided the algorithm into moving sampling (MS) and stepped MS (SMS), and describe the implementation of both algorithms and their comparison. Technical details and common errors concerning the use of Android smartphones to collect Wi-Fi radio beacons were surveyed and discussed. The results of signal sampling experiments performed in a hallway measuring 54 m in length showed that in terms of the amount of time required to complete collection of access point (AP) signals, static sampling (SS; a traditional procedure for collecting Wi-Fi signals) took at least 2 h, whereas MS and SMS took approximately 150 and 300 s, respectively. Notably, AP signals obtained through MS and SMS were comparable to those obtained through SS in terms of the distribution of received signal strength indicator (RSSI) and positioning accuracy. Therefore, MS and SMS are recommended instead of SS as signal sampling procedures for indoor positioning algorithms.