Fingerprint Scanner Research Articles

A multi-function neonatal incubator for low-income countries: Implementation and ab initio social impact

The World health Organization estimates that about 8 million babies die prematurely worldwide. Most of these cases are found in low-income countries (LICs). The use of neonatal incubators has proven to be very useful in the reduction of infant mortality. However, access to this important medical device is not always easy in LICs. This article presents the implementation of a multi-function neonatal incubator with remote monitoring, fingerprint scanner, phototherapy, and heart rate monitoring. In this view, proportional integral (PI) control algorithm and an Android application were developed. Furthermore, an initial social impact of the implemented neonatal incubator usage was performed in some hospitals in Cameroon.

Comparative Study of Fingerprint Patterns of Two Ethnic Groups: A Nigerian Study

Background: The uniqueness of fingerprints have made it possible for anthropologist to carry out studies on ethnic differences, heritability and linguistic relationships among individuals. The study aimed at investigating and comparing fingerprint patterns of the Itsekiri and Urhobo ethnic groups in Warri, South Southern Nigeria. Methods: A total of 30 males and 30 females each from the Itsekiri and Urhobo tribe were investigated. Fingerprints of participants were obtained with a Hewlett placard G4010 fingerprint scanner. Chi-square and Mann Whitney U test were used to test for an association between non parametric variables, significance was accepted at P<0.05. Results: Finding showed that the ulnar loop was the most predominant pattern in the studied population. Fingerprint patterns and total finger ridge count showed dimorphism among the Itsekiris’ as compared to the atd angle of the left fingers seen in the Urhobos’ (p = 0.021; 0.010; 0.038). Significant differences were observed in fingerprint patterns of the right and left thumb alongside the right middle finger among the respective tribes (P=0.047; 0.007; 0.024). Findings also showed a significant difference in TFRC and atd angle between the Itsekiris’ and Urhobos’ (P= 0.010; 0.002; 0.027) . Conclusions: The ulnar loop was dominant among the Itsekiri females and Urhobo males while the whorl and arch patterns were frequent in the Itsekiri males and the Urhobo females.
 Keywords: Delta State; fingerprint; Itsekiri; Urhobo; Warri.

Application of biometric fingerprinting to encourage the active involvement of student teachers in lectures on differentiated instruction

The aim of this article was to establish whether a biometric fingerprint device can be used to accurately record and improve active class participation of student teachers when attending lectures on the application of differentiated instruction. Quantitative and qualitative approaches were used to collect data. In the quantitative study a student-teacher class (n = 180) of a university in South Africa participated during the second and third semesters of the academic year. The quantitative data consisted of the number of student teachers’ active participations recorded using a biometric fingerprint device and data from the second and third semesters were compared. Results were obtained from the calculated number of active participations for which student teachers were rewarded “class bucks” for quality comments. Although class bucks were awarded in both the second and third semesters, the number of student teachers who were actively involved in discussions increased by 18% in the third semester (when biometric fingerprint scanning was implemented). After the completion of their training course, a qualitative approach followed, where participants (n = 36) reflected (through responses to open-ended questions) that they still desired more practice and examples to help them create differentiated activities. It was evident that more activities for practical experiences should be created where student teachers could use various forms of assessment, adjust content, select suitable methods and media, create a suitable learning environment, and identify their learners’ learning barriers and learning preferences to create and apply differentiated activities in the classroom.Keywords: active participation; biometrics; differentiated instruction; student teachers; training

Anti-Spoofing Authentication

Biometrics, One Among the existing security authentication schemes, involves parts of body for identification, hence used in various fields. Due to the level of its simplicity fingerprint recognition remains popular. But the introduction of many technologies has made to lose its uniqueness by just taking a finger print scan, print it on special paper and then swipe. This project is mainly designed to new optical method, which is based on liveness detection on fingers. Infrared pulse sensor which is a new method is employed to enhance image contrast of the finger vein and to know the pattern of blood flow for liveness detection.

Attendance Posting Through Face Recognition

Now a day’s face detection technology is widely used technique. It attracted attention for much valuable application in the market such as face recognition system. Biometric authentication is most important method in security system. Universally used Biometric fingerprint scanner can be bypassed quite easily. It can be broke easily. Biometric face recognition has been introduced to improve the security of a system. Methods such as Motion based and texture based are used for biometric face recognition. But these methods have less robustness and poor generalization ability. But apart from further security issues, this paper presents a new approach to make attendance of the student in class by the face recognition. Now a day’s attendance system is usually done manually or by the biometric fingerprint. Those are mistaken and tedious techniques. So this technique records the student’s participation in classroom consequently and provide facility for teachers for obtaining the data of the student effectively using log to check in and out time

Improving Monitoring of Engagement in HIV Care for Women in Option B+: A Pilot Test of Biometric Fingerprint Scanning in Lilongwe, Malawi

Sustained engagement in HIV care is critical to the success of Option B+ for HIV-infected pregnant women. However, monitoring women's engagement in care across clinics and over time is challenging due to migration and clinic transfers. Improved strategies to identify and monitor women's engagement in HIV care across a network of clinics are needed, but have not been pilot tested. We evaluated the feasibility and acceptability of biometric fingerprint scanning to identify women and monitor HIV visit attendance among women in Option B+. Over a 3-month period, we enrolled HIV-infected pregnant women receiving care at two antenatal clinics in Lilongwe, Malawi and monitored their engagement in care using biometric fingerprint scanning and the standard-of-care electronic medical record (EMR) monitoring system. Biometric data was collected by trained research assistants, who uploaded and synced data across study sites daily using wireless internet. We collected data weekly on the biometric scanner performance, reliability, and usability. We assessed the feasibility and acceptability of using biometric fingerprint scanning to record HIV visits during exit interviews with a sample of participants and healthcare workers and by comparing visit concordance between the biometric fingerprint scanning and EMR systems. We enrolled 314 HIV-infected pregnant women and 51 HCWs (n = 365 total participants). The majority of participants felt the biometric fingerprint scanning system was easy to use (64%), required no additional assistance (69%) and met their expectations (76%). No major issues with data security, privacy, or scanner functionality were reported by HIV-infected women or healthcare workers. Of the 542 HIV visits captured during the study period among women in Option B+, 80% were recorded in the biometric fingerprint system versus 51% in the EMR system (PR 1.57, 95% CI 1.43, 1.72, p-value < 0.05). Among HIV-infected pregnant women engaged in HIV care, biometric fingerprint scanning is a feasible and acceptable way to monitor HIV visits and may improve the ability to monitor women's engagement in HIV care over time and across clinics. Biometric fingerprint scanning should be scaled-up and evaluated as an implementation strategy to support sustained engagement in HIV care for women during the perinatal period.

Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authentication systems is that most fingerprint scanners are vulnerable to presentation attacks by artificial replicas, made from plastic clay, gelatin, silicon, wood glue, etc. One anti-spoofing attack scheme, called real-time fingerprint liveness detection (RFLD), has been proposed to discriminate live or fake fingerprints. Currently, to resolve the presentation attacks, most RFLD solutions all relied on handcrafted feature extraction and selection. The features extracted by manual method are shallow features of the samples; however, autoencoder can automatically learn deep hierarchical semantic features representation of the samples, thus replacing the operations extracted with hand-designed features. In this paper, we apply stacked autoencoder to RFLD to significantly lower the work-force burden of the feature extraction engineering, and our model consists of two parts: parameter pre-training based on unsupervised learning and FLD based on supervised learning. The performance has been verified on two public fingerprint datasets: LivDet 2011 and 2013, and the experimental results indicate that our proposed approach works well for RFLD as well as the detection performance is satisfactory.

Organic Photodetectors and their Application in Large Area and Flexible Image Sensors: The Role of Dark Current

AbstractOrganic photodetectors (OPDs) have gained increasing interest as they offer cost‐effective fabrication methods using low temperature processes, making them particularly attractive for large area image detectors on lightweight flexible plastic substrates. Moreover, their photophysical and optoelectronic properties can be tuned both at a material and device level. Visible‐light OPDs are proposed for use in indirect‐conversion X‐ray detectors, fingerprint scanners, and intelligent surfaces for gesture recognition. Near‐infrared OPDs find applications in biomedical imaging and optical communications. For most applications, minimizing the OPD dark current density (Jd) is crucial to improve important figures of merits such as the signal‐to‐noise ratio, the linear dynamic range, and the specific detectivity (D*). Here, a quantitative analysis of the intrinsic dark current processes shows that charge injection from the electrodes is the dominant contribution to Jd in OPDs. Jd reduction is typically addressed by fine‐tuning the active layer energetics and stratification or by using charge blocking layers. Yet, most experimental Jd values are higher than the calculated intrinsic limit. Possible reasons for this deviation are discussed, including extrinsic defects in the photoactive layer and the presence of trap states. This provides the reader with guidelines to improve the OPD performances in view of imaging applications.

Development of Door Safety Fingerprint Verification using Neural Network

Fingerprints can be used as natural keys. The unique fingerprint of a human being is potential for the security system. This research develops the door safety fingerprint verification using Neural Network. The component used in this research is fingerprint C3 as input processed by Arduino Uno ATMega 2560. After a fingerprint scanning process is done, the microcontroller identifies the fingerprint image. From the microcontroller forwarded the relay module process with solenoid output. Arduino can be read by a computer using the Arduino IDE. Identification during registration via C3 sensor verified using the MATLAB application with the Neural Network method. The design created produces forward and backward solenoid motion, after being given an electric current between 5 to 12 Volts. The MATLAB application will verify the fingerprint image and display the image from the results of the verification done with the user. Research has been tested 10 times with various types of fingerprints with 100% verified so that this study was declared successful.

The Link between Fingerprints, Genetics & Chronic Periodontitis: A Dermatoglyphic Study

Background: Several methods have been employed for diagnosing inflammatory conditions including biomarkers, enzymes and various other clinical parameters. Dermatoglyphics is one such field which has gained entry in Forensic Medicine, Forensic Odontology and in General Medicine for diagnosing chronic inflammatory conditions. Periodontitis is a chronic inflammatory condition associated with destruction of periodontal tissues. This study aimed to assess the relationship between the fingerprint patterns and chronic periodontitis Methods: A total of 60 patients, belonging to both healthy and chronic periodontitis subjects were assessed by a commercially available fingerprint scanner. All the fingerprint images were assessed for the fingerprint pattern and the ridge count manually. The results were tabulated as percentage frequency distribution for the type of fingerprint pattern. The ridge count is shown as mean ± SD. Unpaired t test was applied to test for statistical significance. Results: The frequency distribution showed equal percentage of Radial Loop type of fingerprint pattern in subjects of both Health and Chronic Periodontitis. Statistical analysis showed Plain Whorl type of fingerprint pattern more significant in subjects having Chronic Periodontitis Conclusion: It was concluded that there might be a relationship between type of fingerprint pattern to the chances of a person having Chronic Periodontitis.

Effective Credit Card Forgery Prevention Using Multilevel Authentication

In the present system, credit card forgery is frequently happening, there is no big security is implemented at all. In the proposing system, Multi level security is implemented. It includes the Hex Keypad, GPS, and RFID. The secure information inside can be further secured with secondary system that consisting of formal fingerprint scanner. Modifying process is our Implementation, rather having multiple sensors integrated output in one process, which is actually time consuming, and we implement RFID with Keypad Matrix. POS based payment system. OTP is also verified from the Android application of the user so as to track the location of original user. Payment is made to the receiver only if both the User’s Card & Mobile are in the same location. This system will avoid payment made by the attackers when Credit card is lost or when your details are hacked.

A High‐Resolution Thin‐Film Fingerprint Sensor Using a Printed Organic Photodetector

AbstractOrganic photodetectors (OPDs) have attracted much attention in recent years, due to their promise in large‐area light sensing applications. Here, high‐resolution slot‐die‐coated large‐area bulk heterojunction organic photodiode (OPD) arrays are reported. The OPD uses a novel electron transport layer, indium gallium zinc oxide in combination with a molybdenum oxide top‐electrode. Together, these effectively reduce dark current densities to very low levels of ≈10−7 mA cm−2 at −2 V. The OPDs show linear behavior in a wide range of light intensities and high detectivity values under reverse bias conditions. When coated on a 508 ppi TFT backplane, a high‐quality optical fingerprint scanner capable of imaging in reflection is realized. The optical and electrical properties of the fingerprint sensor are characterized and high‐resolution fingerprint images are obtained.

A Möbius transformation based model for fingerprint minutiae variations

When an individual’s fingerprint is scanned, although the global fingerprint pattern is unchanged, at the local level, between different scans the minutiae pattern may vary. Minutiae translation and rotation are caused by changing finger orientation and position shift during fingerprint acquisition. Minutiae patterns may also suffer non-linear distortion due to finger skin elasticity. Despite a variety of approaches to detecting deformations in fingerprint images, there has been no method available for capturing minutiae variations between two impressions of the same finger in a unified model. In this paper we address this issue by proposing a unified model to represent minutiae variations between fingerprint scans and formulate the changes to minutiae feature patterns. We identify the Möbius transformation as a good candidate for modelling minutiae translation, rotation and non-linear distortion, that is, different types of minutiae variations are described in a single model. Not only do we mathematically prove that the Möbius transformation based model is a unified model for capturing minutiae variations, but we also experimentally verify the effectiveness of this model using a public database.

A Smart Watch for Women Security

The paper describes a prototype of wearable a device enhanced with GSM technology for protecting women during physical harassment or robbery. This device is having the sensing unit, in the form of a watch and is responsible for monitoring the heartbeat of the individual. It is equipped with a finger print scanner, to wear and remove the device. It also consists of RTC (Real Time Clock) which will display the clock in the system. Heartbeat sensor is used to monitor the heartbeat of the women. If any harassment takes place to women, naturally heartbeat will increase then the wearable device sends the SMS to respective people or police whose numbers are fed in the system. It will send the messages only after a preset delay, so that error can be avoided, during the course of exercise or walking fast.

71‐3: Organic photolithography for displays with integrated fingerprint scanner

Patterning organic materials by photolithography enables not only high‐resolution, side‐by‐side RGB OLED arrays, but also the introduction of organic photodetector in the same frontplane. We show patterned active matrix displays processed on flexible backplane. Furthermore, we demonstrate OPD patterning proof‐of‐concept. These are the building blocks allowing in‐cell optical fingerprint sensor.

PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition

With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore patches. For verification, the match score is generated by comparing pore descriptors obtained from a pair of fingerprint images in bi-directional manner using the Euclidean distance. The proposed approach for high-resolution fingerprint recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI) and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most importantly, it achieves lower FMR1000 and FMR10000 values than the current state-of-the-art approach on both the datasets.

Real-time Face Recognition Under Different Environment

The problems related to security and safety have motivated us to add some contribution to face recognition work by making it real-time process with more automation and robustness. As we know, fingerprint scanning is more manual as it needs human intervention. So Face is an unique identity of a person which is used by surveillance system. The aim of these surveillance systems is to provide an automatic interpretation of scenes by identifying correct person for human safety purposes. But due to lots of variation due to unconstrained environment and limitations face recognition becomes difficult task for real-time applications, where we will be considering few main problems like illumination, occlusion, head-pose and facial expressions will be solved in our work to improve real-time face recognition process. We will be using MTCNN and pre-trained Inception- ResNet model of CNN.

A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation.

BackgroundPatient verification by unique identification is an important procedure in health care settings. Risks to patient safety occur throughout health care settings by failure to correctly identify patients, resulting in the incorrect patient, incorrect site procedure, incorrect medication, and other errors. To avoid medical malpractice, radio-frequency identification (RFID), fingerprint scanners, iris scanners, and other technologies have been implemented in care settings. The drawbacks of these technologies include the possibility to lose the RFID bracelet, infection transmission, and impracticality when the patient is unconscious.ObjectiveThe purpose of this study was to develop a mobile health app for patient identification to overcome the limitations of current patient identification alternatives. The development of this app is expected to provide an easy-to-use alternative method for patient identification.MethodsWe have developed a facial recognition mobile app for improved patient verification. As an evaluation purpose, a total of 62 pediatric patients, including both outpatient and inpatient, were registered for the facial recognition test and tracked throughout the facilities for patient verification purpose.ResultsThe app was developed to contain 5 main parts: registration, medical records, examinations, prescriptions, and appointments. Among 62 patients, 30 were outpatients visiting plastic surgery department and 32 were inpatients reserved for surgery. Whether patients were under anesthesia or unconscious, facial recognition verified all patients with 99% accuracy even after a surgery.ConclusionsIt is possible to correctly identify both outpatients and inpatients and also reduce the unnecessary cost of patient verification by using the mobile facial recognition app with great accuracy. Our mobile app can provide valuable aid to patient verification, including when the patient is unconscious, as an alternative identification method.

Enhanced PAD security Mechanism Using Fingerprint and Face Scanning

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Biometric Technology and Ethics: Beyond Security Applications

Biometric technology was once the purview of security, with face recognition and fingerprint scans used for identification and law enforcement. This is no longer the case; biometrics is increasingly used for commercial and civil applications. Due to the widespread diffusion of biometrics, it is important to address the ethical issues inherent to the development and deployment of the technology. This article explores the burgeoning research on biometrics for non-security purposes and the ethical implications for organizations. This will be achieved by reviewing the literature on biometrics and business ethics and drawing from disciplines such as computer ethics to inform a more robust discussion of key themes. Although there are many ethical concerns, privacy is the key issue, with associated themes. These include definitions of privacy, the privacy paradox, informed consent, regulatory frameworks and guidelines, and discrimination. Despite the proliferation of biometric technology, there is little empirical research on applied biometrics and business ethics. As such, there are several avenues for research to improve understanding of the ethical implications of using this technology.