Fingerprint Matching Research Articles

Privacy protection based on binary fingerprint compression

This paper proposes a novel system to protect the fingerprint database based on compressed binary fingerprint images. In this system, the user can store private data with high capacity. First, the grayscale fingerprint image is transformed into a binary bitstream. Then, the binary bitstream is compressed using run-length encoding and Huffman encoding technique to create a sparse space to accommodate private data. Finally, the new image constructed by the obtained binary bitstream is encrypted with an encryption key. For fingerprint matching, it is only need to decrypt and decompress the encrypted image in the database to obtain the binary fingerprint image. When the matching is passed, the private data can be extracted to a user with data-embedding key. If the matching is failed, the private data cannot extracted for the purpose of privacy security. Even if a leakage of the encryption key occurs, this system can still protect the privacy data of the user due to the existence of the data-embedding key. Meanwhile, the encoding and decoding phases are real time, which guarantee the practicability of the proposed system.

Rancang Bangun Sistem Presensi Menggunakan Sidik Jari (Studi Kasus Pada Lembaga Pemasyarakatan Klas II-A Palu)

Dermatoglyphic has been widely applied to recognize a person's identity, because people basically have something unique or characteristic possessed only by themselves. This raises the idea of making it a unique identity. This is useful for employees presence applications, especially at Lembaga Pemasyarakatan Klas IIA Palu which is still doing manual presence. This system provides the division of labor time and wereable to detect the fingerprints of employees using a fingerprint scanner. The system was made in several stages of the input fingerprint image, binarization fingerprint image, fingerprint image quality improvement, and matching fingerprint image. Improvements to the quality of the fingerprint image consists of the surgical process of mathematical morphology opening and erosion. The detection process of the fingerprint image used euclidean distance measurement points minutiae. The test results indicate that the presence system accuracy using euclidean distance measurement could detect the fingerprint image by 89% from 20 fingerprint data with 5 times testing for each data.

An Efficient Fingerprint Matching by Multiple Reference Points

An Efficient Fingerprint Matching by Multiple Reference Points

Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System

Indoor magnetic-based positioning has attracted tremendous interests in recent years due to its pervasiveness and independence from extra infrastructure. Existing methods for indoor magnetic-based positioning are either point-based fingerprint matching or sequence-based fingerprint matching using the raw magnetic field strength. However, the magnetometers in smartphones are vulnerable to a few factors such as user's postures and walking speed, which causes the magnetic field strength corresponding to a location often shift in time or exhibit local distortions, thus greatly limits the positioning performance of existing methods rely on raw magnetic field strength. To this end, we observe the differences among magnetic field strength sequences are mainly attributed to small local segments, and design a new sequence-based fingerprint based on the differences among small local segments of raw MFS sequence to represent raw MFS sequence for indoor positioning. To demonstrate the utility of our proposed sequence-based fingerprint, we have performed a comprehensive experimental evaluation on two datasets, the results show that the proposed approach can significantly improve positioning performance compare with baseline methods.

基于正交频分复用调制的可见光通信指纹匹配定位算法

基于正交频分复用调制的可见光通信指纹匹配定位算法

Latent Fingerprint Matching Using Distinctive Ridge Points

The way that forensic examiners compare fingerprints highly differs from the behaviour of current automatic fingerprint identification algorithms. Experts usually use all the information in the fingerprint, not only minutiae, while automatic algorithms don’t. Partial (especially latent) fingerprint matching algorithms still report low accuracy values in comparison to those achieved by experts. This difference is mainly due to the features used in each case. In this work, a novel approach for matching partial fingerprints is presented. We introduce a new fingerprint feature, named Distinctive Ridge Point (DRP), combined with an improved triangle-based representation which also uses minutiae. The new feature describes the neighbouring ridges of minutiae in a novel way. A modified version of a fingerprint matching algorithm presented in a previous work is used for matching two triangular representations of minutiae and DRPs. The experiments conducted on NIST27 database with a background added of 29000 tenprint impressions from NIST14 and NIST4 databases showed the benefits of this approach. The results show that using the proposal we achieved an accuracy of 70.9% in rank-1, improving in an 11% the accuracy obtained using minutiae and the reference point. This result is comparable with the best accuracy reached in the state of the art while the amount of features is reduced.

Fingerprint Access Control for Wireless Insulin Pump Systems Using Cancelable Delaunay Triangulations

An insulin pump is a small wearable medical gadget which can mimic the way a healthy pancreas functions by providing continuous subcutaneous insulin infusion for the patient. However, in the current products, the access to the pump is not securely controlled, rendering the pump insecure to harmful or even lethal attacks, such as those that lead to the privacy breach of the patient and the delivery of abnormal dosage of insulin to the patient. In a conventional symmetric key-based security solution, how to distribute and manage the key is quite challenging, since the patient bearing an insulin pump may visit any hospital or clinic to receive treatment from any qualified doctor. In order to prevent malicious access to the pump, in this paper, we propose a Fingerprint-based Insulin Pump security (FIPsec) scheme which requires an entity to first pass the fingerprint authentication process before it is allowed to access the insulin pump. With this scheme, the request from an adversary to access the pump will be blocked thereby protecting the pump from the possibility of being subjected to serious attacks during the post-request period. In the scheme, a cancelable Delaunay triangle-based fingerprint matching algorithm is proposed for the insulin pump, which has capabilities to resist against nonlinear fingerprint image distortion and the influence of missing or spurious minutiae. In order to evaluate the performance of the proposed fingerprint matching algorithm, we perform comprehensive experiments on FVC2002 DB1 and DB2 fingerprint databases. The results show that the FIPsec scheme can achieve a reasonably low equal error rate and thus becomes a viable solution to thwarting unauthorized access to the insulin pump.

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.

Implementasi Sidik Jari sebagai Otentikasi Parkir Kendaraan Menggunakan Raspberry Pi

Otentikasi keluar masuk kendaraan pada tempat parkir merupakan salah satu permasalahan yang masih perlu dieksplorasi. Sebagian besar tempat parkir menggunakan otentifikasi mencatat nomor kendaraan pada kartu parkir. Petugas memverifikasi nomor kendaraan yang tertera dikartu parkir dengan nomor kendaraan yang tertera pada Surat Tanda Nomor Kendaraan (STNK). Metode otentifikasi tersebut membutuhkan ketelitian dari petugas parkir. Penelitian yang dilakukan memanfaatkan identifikasi sidik jari pengemudi kendaraan dan pengenalan plat nomor kendaraan melalui foto oleh petugas parkir sebagai otentikasi. Perekaman sidik jari dan foto kendaraan dilakukan pada saat kendaraan masuk. Identifikasi ini digunakan pada saat kendaraan akan keluar. Sistem dibangun dari beberapa perangkat, yaitu sensor ultrasonic untuk mendeteksi keberadaan kendaraan. Sensor fingerprint untuk mendeteksi sidik jari yang dilengkapi dengan storage untuk menyimpan template sidik jari. Webcam untuk pengambilan foto kendaraan sebagai verifikasi pada saat kendaraan keluar tempat parkir. Sedangkan pintu portal digerakan oleh motor servo. Semua sistem dikendalikan menggunakan Raspberry Pi 3 menggunakan bahasa pemprogram python. Hasil pengujian pada sensor ultrasonic mampu mendeteksi keberadaan kendaraan pada jarak 10-200 cm dengan tingkat keakurasian 96%. Motor servo berputar posisi 0° jika nilai DutyCycle diatur 2,5 dan berputar 90° jika nilai DutyCycle 7,5. Posisi webcam berada 2 meter dari letak sensor fingerprint. Posisi jari yang ditekan menghasil proses yang lebih cepat dalam mengidentifiaksi karakter sidik jari pada saat perekaman dan pencocokan sidik jari.

Bio-medical and latent fingerprint enhancement and matching using advanced scalable soft computing models

Latent fingerprints are acquired from crime places which are utilized to distinguish suspects in crime inspection. In general, latent fingerprints contain mysterious ridge and valley structure with nonlinear distortion and complex background noise. These lead to fundamentally difficult problem for further analysis. Hence, the image quality is required for matching those latent fingerprints. In this work, we develop a model for enhancement of latent fingerprint and matching algorithm, which requires manually marked (ground-truth) ROI latent fingerprints. This proposed model includes two phases (i) Latent fingerprints contrast enhancement using type-2 intuitionistic fuzzy set (ii) Extract the minutiae and Scale Invariant Feature Transformation (SIFT) features from the latent fingerprint image. For matching, these algorithms have been figured based on minutiae and SIFT points which inspect n number of images and the scores are calculated by Euclidean distance. We tested our algorithm for matching, using some public domain fingerprint databases such as Fingerprint Verification Competition − 2004 (FVC-2004) and Indraprastha Institute of Information Technology (IIIT)-latent fingerprint which indicates that by fusing the proposed enhancement algorithm, the matching precision has fundamentally moved forward.

2-D Phase Demodulation for Deformable Fingerprint Registration

Fingerprint matching with elastic distortion is very challenging to deal with, and severe fingerprint distortion usually leads to false non-matches. This paper proposes a phase-based registration algorithm which can effectively eliminate the distortion between fingerprints and therefore is beneficial to the subsequent fingerprint matching. The key of the proposed algorithm is to reconstruct the distortion field through unwrapping phase difference between two fingerprints. Experiments on FVC2004, Tsinghua distorted fingerprint database, and NIST SD27 demonstrate that our algorithm outperforms other fingerprint registration methods and significantly improves matching accuracy.

Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases.

FPR using machine learning with multi‐feature method

Biometrics authentication is considered as most secure and reliable method to recognise and identify person's identity. Researchers put efforts to find efficient ways to secure and classify the solutions to biometric problems. In this category, fingerprint recognition (FPR) is most widely used biometric trait for person identification/verification. The present work focuses an FPR technique, which uses the grey-level difference method, discrete wavelet transforms and edge histogram descriptor for fingerprint representation and matching. Wavelet shrinkage used for noise removal in the image. Ridge flow estimation is calculated using the gradient approach. SVM and Hamming distance similarity measures are used for recognition. The experiment result has been tested on the standard 2000–2004 fingerprint verification competition dataset and the accuracy of proposed algorithm was reported to be well above 98%.

INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

Abstract. With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.

Video copy detection by conducting fast searching of inverted files

Fast content-based video copy detection is challenging because video databases have become extremely large. Conventional video-fingerprint-based copy detection systems that use the inverted-file approach involve many similarity computations based on the Hamming distance. To overcome this problem, a novel fast searching strategy for inverted files is proposed in this paper. The strategy involves simple table look-up and word counting operations for the fingerprint matching process. The similarity of video fragments is based on the number of matched fingerprints among all video candidates. In this method, the offset time is used, and fingerprints are ordered to further select the matched fingerprints from the video candidates. Moreover, a novel regional average fingerprint that is compatible with the proposed fast searching strategy is proposed. An experimental video copy detection system was used with the proposed algorithms, and the proposed algorithms were compared with other state-of-the-art fingerprinting algorithms on TRECVID 2011 dataset for different types of video distortions. In addition, VCDB dataset was also used to demonstrate the accuracy and efficiency of the proposed fast searching strategy while using inverted files to demonstrate the practicality of the method for a large database. The proposed system achieved higher accuracy on VCDB dataset with considerably higher operation speed compared with conventional inverted-file-based searching methods.

Latent Fingerprint Enhancement Based on Directional Total Variation Model with Lost Minutia Reconstruction

Image enhancement plays an important role in biometric systems, this paper presented automatic latent fingerprint segmentation and matching. While considerable progress has made in both rolled and plain fingerprint image enhancement, latent fingerprint enhancement is a challenging problem due to the poor image quality of latent fingerprint with unclear ridge structures and various overlapping patterns, along with the presence of structured noise. Prior to latent fingerprint segmentation and feature extraction, latent fingerprint image enhancement is important to suppress various types of noise and to clarify the ridge structure. This paper reviews the current techniques used for latent fingerprint enhancement and presents a hybrid model which combines the edge directional total variation model (EDTV) and quality image enhancement with lost minutia reconstruction. The NIST SD27 database is used to test the performance of the proposed techniques with RMSE and PSNR. The proposed technique is effectively clarify input latent fingerprint images and eliminate noise in good, bad and ugly latent fingerprint images. A statistically significant difference, which focused on the mean lengths of PSNR and RMSE for different categories of latent fingerprint, images (good, bad and ugly). The proposed technique performs well for the good latent fingerprint images compare to bad and ugly images. Enhancement respectively presents RMSE averages of 0.018373, 0.022287, and 0.023199 for the good, bad and ugly image SD27 image set, as opposed to 82.99068, 81.39749, and 81.07826 for PSNR. The proposed enhancement technique improved the matching accuracy of latent fingerprint images by about 30%.

Fingerprint Pattern of Matching Family with GLCM Feature

In this research, fingerprint pattern matching is done to find out whether there is the similarity between parent and child fingerprint pattern. An important step in fingerprint matching is the fingerprint pattern search and matching. Fingerprint data is used by 11 families from various families. The method used in fingerprint feature extraction is GLCM. The GLCM angle used is 0o, and the features used are contrast, homogeneity, correlation, and energy. For fingerprint pattern matching use minutiae score. From the results obtained GLCM has been widely used in fingerprint texture analysis. This study proves that the proposed method for matching fingerprints on parents and children gets the most dominant pattern is the loop pattern.

RaspiReader: Open Source Fingerprint Reader.

We open source an easy to assemble, spoof resistant, high resolution, optical fingerprint reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the fingerprint research community a seamless and simple method for quickly prototyping new ideas involving fingerprint reader hardware. In particular, we posit that this open source fingerprint reader will facilitate the exploration of novel fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.

Positioning and guiding educational robots by using fingerprints of WiFi and RFID array

Positioning educational robots in the indoor ambiance is an important and basic function for robots to provide intelligent educational services for users, and it is still an open challenge problem. This paper proposes to position indoor robots by using fingerprints of wireless fidelity (WiFi) and radio-frequency identification (RFID) array in the complementary way. The fingerprint of WiFi is first used to position educational robots (Erob) in a large area and to guide robots to a place close to the target location. Then, the fingerprint of RFID array is used to guide Erob to the target location with a small discrepancy. It proved that the designed layouts of WiFi devices and RFID array can have fingerprint matching to estimate the position of Erob. The proposed positioning method can guide Erob to the target location fast and accurately so that robots can provide multiple services based on it.

An IBeacon-Based Location System for Smart Home Control.

Indoor location and intelligent control system can bring convenience to people’s daily life. In this paper, an indoor control system is designed to achieve equipment remote control by using low-energy Bluetooth (BLE) beacon and Internet of Things (IoT) technology. The proposed system consists of five parts: web server, home gateway, smart terminal, smartphone app and BLE beacons. In the web server, fingerprint matching based on RSSI stochastic characteristic and posture recognition model based on geomagnetic sensing are used to establish a more efficient equipment control system, combined with Pedestrian Dead Reckoning (PDR) technology to improve the accuracy of location. A personalized menu of remote “one-click” control is finally offered to users in a smartphone app. This smart home control system has been implemented by hardware, and precision and stability tests have been conducted, which proved the practicability and good user experience of this solution.