Mobile Fingerprint Research Articles

Mobile Finger Print Verification and Automatic Log in Platform Using Blockchain

Abstract: Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which however brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric data is encrypted and outsourced using Block Chain. To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The Block Chain performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud

Door Lock Security System Using Recent Technology

Abstract: In terms of house security, the door is pivotal. To keep the hearthstone secure, the proprietor will keep the door locked at all times. Still, owing to a rush when leaving the house, the proprietor may forget to lock the door, or they may be doubtful if they’ve closed the door or not. Wireless security grounded operation have fleetly increased due to the dramatic enhancement of ultramodern technologies. Numerous access control systems were designed and/or enforced grounded on different types of wireless communication technologies by different people. Radio Frequency identification (RFID) is a contactless technology that’s extensively used in several diligences for tasks like access control system, book shadowing in libraries, tollgate system, forced chain operation, and so on. For enforcing this design, we will be using Arduino mega 2560 pro mini, a fingerprint sensor, Keypad module. ESP-32 CAM module, RFID sensor, solenoid lock and ESP8266. We have also created an application for monitoring and controlling the security features of the door lock. We can also open the door through mobile fingerprint. Keywords: Arduino mega 2560 pro mini, ESP32-CAM, ESP8266, Fingerprint sensor, Keypad module, RFID sensor.

Decentralized Remote Voting System

In Our idea we are going to build Decentralized Distributed voting platform Using Blockchain technology that could be a web application or Mobile App. Authentication: Authentication can be done by Aadhar Card using Mobile number OTP or Fingerprint on successfully authentication unique blockchain address will generate for particular voter. Data Sync: Data of portal need to be sync with voting booths. So, EVM also need to be Digitalize that connected could be connected & sync to our portal’s decentralized databases. These are Two solution to overcome vote lost still there are some percentage of vote lost because of those people who’s Aadhar Card not Updated and they not receive OTP on Mobiles. Then there is one option for Authentication that is fingerprint scan. So we want type of machine which easily available for common people and that machine have fingerprint scanner And that Machine is nothing but ATM machine, means Our third solution is some changes in ATM machine software will Convert ATM into EVM.

Towards Using Police Officers' Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets.

Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included.

Walidacja Mobilnego Stanowiska Daktyloskopijnego w ramach projektu HIT-NET

Mobility is associated with freedom of movement and, in the case of computers, also with wireless connection to the Internet for fast data exchange. Professionals in various industries or formations are increasingly seeking to implement such functionality in the equipment they use. Police use mobile terminals , devices forrapid confirmation of individuals’ identities (Kot, Tomaszycki, 2015, pp. 340 and 343), and a mobile police workstation, but are not equipped with a mobile fingerprint workstation. The first prototype workstation of this kind was purchased under the NCBR-funded project no. DOB-BIO9/13/01/2018 (HIT-NET) and validated at the Fingerprint Examination Department of the Central Forensic Laboratory of the Police. The article provides the results of the tests conducted and suggests directions for modernization of such setups.

Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning

Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

Metadata filtering for user-friendly centralized biometric authentication

While biometric authentication for commercial use so far mainly has been used for local device unlock use cases, there are great opportunities for using it also for central authentication such as for remote login. However, many current biometric sensors like for instance mobile fingerprint sensors have too large false acceptance rate (FAR) not allowing them, for security reasons, to be used in larger user group for central identification purposes. A straightforward way to avoid this FAR problem is to either request a user unique identifier such as a device identifier or require the user to enter a unique user ID prior to making the biometric matching. Usage of a device identifier does not work when a user desires to authenticate on a previously unused device of a generic type. Furthermore, requiring the user at each login occasion to enter a unique user ID, is not at all user-friendly. To avoid this problem, we in this paper investigate an alternative, most user-friendly approach, for identification in combination with biometric-based authentication using metadata filtering. An evaluation of the adopted approach is carried out using realistic simulations of the Swedish population to assess the feasibility of the proposed system. The results show that metadata filtering in combination with traditional biometric-based matching is indeed a powerful tool for providing reliable, and user-friendly, central authentication services for large user groups.

Evaluation of the QIAGEN 140-SNP forensic identification multiplex from latent DNA using massively parallel sequencing

ABSTRACTDirect PCR can be used to successfully generate full STR profiles from latent DNA. However, some substrates have been shown to be more problematic and in some cases only partial profiles are recovered. As latent DNA is present on the surface of objects in very low quantity, and potentially low quality, the fragment lengths targeted by STR typing may be too large to successfully amplify all markers. As an alternative, QIAGEN have developed a 140-SNP multiplex that targets much shorter amplicons and generates extremely low probabilities of any two unrelated individuals having identical genotypes. Here, we present the first forensic identification SNP data from latent DNA using massively parallel sequencing. We applied the QIAGEN 140-SNP forensic identification multiplex to swabs collected from multiple substrates (including mobile phone, fingerprint, wire, zip-lock bag and SIM card) and multiple donors.

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.

Blockchain-based mobile fingerprint verification and automatic log-in platform for future computing

Door locks and user authentication are the major issues in the current banking industry. There are a number of user future computing techniques, and security is especially essential to these methods. Existing digital door locks have the problem of opening easily with a stolen PIN number or by electrical shock. Thus, this study proposed and implemented an integrated automatic log-in platform based on mobile fingerprint recognition by applying the blockchain theory. As a result of this research, a convenient, integrated automatic log-in platform with powerful security has been constructed using a smartphone-based fingerprint recognition function. There are three major functions of the platform. First, it is possible to authenticate the user in PC, mobile device, and IoT environments through fingerprint recognition. Second, the platform includes SDK to develop application software for user authentication and IoT services. Last is its strengthened security using the blockchain theory to prepare against tampering/forging/leaking of a user’s fingerprint information by hackers.

Slide: Towards Fast and Accurate Mobile Fingerprinting for Wi-Fi Indoor Positioning Systems

The deployment of Wi-Fi fingerprint-based indoor positioning systems is severely hindered by the lack of an efficient and low-cost way to establish a signal fingerprint database. In this paper, we present a novel fingerprinting method, slide , that can collect fingerprints in a fast and accurate way. Slide uses a commodity flashlight and a smartphone to achieve linear positioning . This allows automatic mapping from the received signal strength to the position on a line, serving as a building block for fingerprinting in general environments. Slide also features a channel-based scanning method, which acquires fingerprint location after each Wi-Fi channel scanning, to mitigate the fingerprint misalignment problem found in the general mobile fingerprinting. Quantitative analysis and experimental results show that slide is faster than the manual fingerprinting method by up to an order of magnitude with comparable positioning accuracy, and is also more efficient than state-of-the-art mobile fingerprinting methods.

Auth ‘n’ Scan

Recent commodity smartphones have biometric sensing capabilities, allowing their daily use for authentication and identification. This increasing use of biometric systems motivates us to design an opportunistic way to sense user's additional physiological or behavioral data. We define this concurrent physiological or behavioral data sensing during biometric authentication or identification as dual-purpose biometrics. As an instance of dual-purpose biometrics, we develop photoplethysmography (PPG) sensing during mobile fingerprint authentication, called Auth ‘n’ Scan. Our system opportunistically extracts cardiovascular information, such as a heart rate and its variability, while users perform phone unlock of a smartphone. To achieve this sensing, our Auth ‘n’ Scan system attaches four PPG units around a fingerprint sensor. The system also performs noise removal and signal selection to accurately estimate cardiovascular information. This paper presents the hardware implementation and signal processing algorithm of our Auth ‘n’ Scan prototype. We also report our system evaluations with 10 participants, showing that, despite a little low precision (a standard deviation of 3--7), estimation of heart rates with high accuracy (under a mean error of 1) is possible from PPG data of five seconds and longer if their baseline information is given. We discuss the feasibility of opportunistic PPG sensing in mobile fingerprint authentication.

Use Intention of Mobile Fingerprint Payment between UTAUT and DOI in China

Use Intention of Mobile Fingerprint Payment between UTAUT and DOI in China

Using biometric-based identification systems in Brazil: A review on low cost fingerprint techniques on-the-go

Automatic authentication has become an essential service in several public areas. However, although the technology related with this kind of service has evolved, the price tag of its use is not affordable for most countries. In the so-called “under developed” counties, such as Brazil, South Africa and India, for example, registration systems are often paper-based and/or cover only a fraction of the population. Thus, the reality is that there is an increasing gap into the usage of such technologies amongst different countries and it can be a factor that makes development more difficult and, therefore, less inclusive. One of the main technologies used for automatic identity prediction is based on biometrics analysis, which can distinguish physical or behavioural features to help overcome the traditional paper-based identity systems. Despite the limitations already mentioned, Brazil is known to have introduced several different uses of biometric-based technologies for authentication. However, the use of these technologies is not always ideal and, since the population size is a key factor, it is essential to select the most affordable option which is not necessarily the most adequate for the country's needs. This paper will focus on establishing what biometric-based solutions exist in Brazil today, highlighting the main challenges, as well as briefly proposing a new prototype for mobile fingerprint acquisition.

Simultaneous navigation and pathway mapping with participating sensing

Navigating pedestrians to the expected location in an unfamiliar indoor space, like museum, shopping mall, and airport, is often time-consuming and grueling. Anchor position information or a radio map based navigation strategies are useful in some occasions, but such anchors or maps may not be always available for everyone when needed. In this work, We study the possibility of using hints extracted from off-the-shelf sensors on smartphone and widely deployed WLANs to provide a reliable grass-roots solution for indoor navigation. The proposed simultaneous navigation and pathway mapping approach, EasyPop, overcomes these restrictions, i.e., neither placing assistant devices in advance nor the floor plan are needed. Based on Wi-Fi or cellular signals, site-specific calibration in radio-frequency fingerprinting is received by automatically path mapping. Mobile fingerprints are generated on wireless client by probing the neighbored Wi-Fi APs in predefined period, as well as the sensor hints with respect to spatial---temporal features. Pathways can be generated via incremental construction pattern while quickly reducing the expected distance of a randomly-chosen point from POIs. By leveraging a Bayesian probability model to pathway mapping, the optimal direction of motions is demonstrated step by step by crowdsourcing. EasyPop is evaluated in multiple indoor scenarios involving six volunteers with different data density. The experimental results show that the proposed method has comparable performance to the-state-of-arts without requiring any a priori.

Mobile fingerprint biometrics ID air crash victims

Mobile fingerprint biometric units have contributed to identifying victims of the AirAsia Flight QZ8501 that went missing in December 2014, reports Mobile ID World.

US and NZ police forces roll out mobile fingerprint biometrics

US and NZ police forces roll out mobile fingerprint biometrics

Extreme learning machine for user location prediction in mobile environment

PurposePrediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.Design/methodology/approachPrediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k‐Nearest Neighbor approaches.FindingsWiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k‐Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k‐Nearest Neighbor approach.Originality/valueA new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).

Going mobile

Biometric technology looks ready to hit the road as mobile fingerprinting becomes a reality for the police, event organisers, and even theme parks. John Elliott, principal consultant with Consult Hyperion and head of the firm's Public Sector Practice, reports.