To see the other types of publications on this topic, follow the link: Automated Fingerprint Identification System.
Journal articles on the topic 'Automated Fingerprint Identification System'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Automated Fingerprint Identification System.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Patil, Suraj Uday. "Automated Fingerprint Identification System." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 279–84. http://dx.doi.org/10.22214/ijraset.2024.64498.
Full textAbstract:
Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security
2
Nwe, Ni Kyaw, Kyaw Naing Kyaw, and Myo Nwe Wai Myat. "Identification of Persons by Fingerprint using Huffman Coding Algorithm." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1208–11. https://doi.org/10.5281/zenodo.3590611.
Full textAbstract:
Fingerprint system is one of the most common biometric features used for personal identification and verification. There are many techniques used for finding the features of fingerprint when it matches the other images of fingerprint. This system must input the fingerprint for the corresponding profile. It converts the binary image. This image will convert the Huffman code approach and to generate codes for input fingerprint. The system has the profile information along with their associated fingerprints. When the system identified the fingerprint, the system will display the associated profile information for the corresponding image. Huffman compression technique was the most accurate technique for compression of fingerprint image. Nwe Ni Kyaw | Kyaw Kyaw Naing | Myat Myo Nwe Wai "Identification of Persons by Fingerprint using Huffman Coding Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26600.pdf
3
Shen, Xuening, Minde Cheng, Qingyun Shi, and Guisheng Qiu. "A new automated fingerprint identification system." Journal of Computer Science and Technology 4, no. 4 (1989): 289–94. http://dx.doi.org/10.1007/bf02943110.
Full text
4
Kirvel, Vitali. "Fingerprint analysis (AFIS) and biometric system of identification by fingerprints: issue of artificial papillary pictures." Общество и инновации 1, no. 3 (2021): 129–45. http://dx.doi.org/10.47689/2181-1415-vol1-iss3-pp129-145.
Full textAbstract:
The article discusses the theoretical and applied aspects, as well as prospects for the development of fingerprint examination, fingerprint records (automated fingerprint identification systems-AFIS) in the disclosure and uncovering of crimes, as well as biometric identification systems by fingerprints to protect personal data, control access to corporate and personal information, time tracking in the information sector of the economy.
 The paper presents methods form an ufacturing models with artificial papillary patterns (falsification of papillary patterns of fingerprints), the results of experiments that consisted in creating a model (dummy) of the nail phalanx of the finger with an artificial papillary pattern and verification of a biometric scanner using biometric technologies.
5
Sun, Hong. "Design of Embedded Automated Fingerprint Identification System Based on DSP." Advanced Engineering Forum 1 (September 2011): 97–101. http://dx.doi.org/10.4028/www.scientific.net/aef.1.97.
Full textAbstract:
The automated fingerprint identification algorithm has high time and space complexity in the embedded system. How to reduce the complexity is one of the hot research topics. The process of fingerprint identification and choice of algorithm platform are analyzed in the paper. Design of embedded fingerprint identification hardware system based on DSP, including the selection of microprocessor and fingerprint sensor and the communication between them, is introduced in detail. In additional, main software composition and flow are explained. At last, serial peripheral interface communication is simulated.
6
Singla, Nancy, Manvjeet Kaur, and Sanjeev Sofat. "Automated latent fingerprint identification system: A review." Forensic Science International 309 (April 2020): 110187. http://dx.doi.org/10.1016/j.forsciint.2020.110187.
Full text
7
Zhang, Rui, and Feng Wang. "Research on Cogent Automatic Fingerprint Identification System." Advanced Materials Research 605-607 (December 2012): 1741–47. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1741.
Full textAbstract:
Cogent Automatic Fingerprint Identification System (CAFIS) was researched by Cogent Co. Ltd. This system filtrated and matched fingerprints feathers automatically by computer to search for similar fingerprints, which are identified by specialists finally. It can provide help for criminal cases. About CAFIS, this paper introduces the principle, fingerprint identification process and the system workflow. Then, it researches five functions modules; they are image acquisition, screening, feathers matching, scoring and sorting; and introduce the algorithm of feather matching briefly. Experimental results show that it has many advantages, such as the big capacity of database, quick matching speed, high accuracy, etc.
8
KWAN, PAUL W., JUNBIN GAO, YI GUO, and KEISUKE KAMEYAMA. "A LEARNING FRAMEWORK FOR ADAPTIVE FINGERPRINT IDENTIFICATION USING RELEVANCE FEEDBACK." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 01 (2010): 15–38. http://dx.doi.org/10.1142/s0218001410007841.
Full textAbstract:
In recent years, law enforcement personnel have greatly been aided by the deployment of Automated Fingerprint Identification Systems (AFIS). These systems largely operate by matching salient features automatically extracted from fingerprint images for their decision. However, there are two major shortcomings in current systems. First, the result of identification depends primarily on the chosen features and the algorithm that matches them. Second, these systems cannot improve their results by benefiting from interactions with seasoned examiners who often can identify minute differences between fingerprints beyond that is capable of by current systems. In this paper, we propose a system for fingerprint identification that incorporates relevance feedback. We show that a persistent semantic space over the database of fingerprints can be incrementally learned. Here, the learning module makes use of a dimensionality reduction process that returns both a low-dimensional semantic space and an out-of-sample mapping function, achieving a two-fold benefits of data compression and the ability to project novel fingerprints directly onto the semantic space for identification. Experimental results demonstrated the potential of this learning framework for adaptive fingerprint identification.
9
D.Deepakraj. "An Efficient Automatic Fingerprint Authentication System Using a Template Protection Technique." Journal of Information Systems Engineering and Management 10, no. 2s (2025): 140–49. https://doi.org/10.52783/jisem.v10i2s.208.
Full textAbstract:
The disclosure of biometric template data is one of the possible weaknesses in a biometric system, posing major security and privacy risks. The majority of template protection methods on the market fall short of meeting all the necessary specifications for a workable biometric system, including high matching accuracy, security, privacy, and revocability. Research on automated fingerprint-based identification began in the early 1960s since fingerprints have been a vital tool for forensics and law enforcement for more than a century. Our proposal includes a system that uses extraction of minutiae approach to verify fingerprints and an automated system that takes attendance. Unimodal fingerprint biometric systems, which analyze distinctive sequences to protect authentication information, deal with issues including noisy data, non-universality, intra-class deviation, and susceptibility to spoof attacks. Multimodal fingerprint biometric systems overcome these issues by compensating for the shortcomings of different biometric sources. Our test findings demonstrate that, while maintaining template security, the suggested multi-biometric template protection strategy outperforms its unibiometric equivalents regarding verification outcomes.
10
Rwamurangwa, Godfrey. "Matching of Partial Middle Phalangeal Prints of the Finger for Identification of an Offender: The Use of Automated Biometric Identification System (ABIS)." International Journal of Forensic Sciences 9, no. 4 (2024): 1–4. https://doi.org/10.23880/ijfsc-16000424.
Full textAbstract:
Friction ridge impression from the areas of the fingers or hands that are not from the fingertips are called non-distal prints, which include palm prints, middle and proximal phalangeal prints, and interphalangeal prints. The presence of these fingerprints is less compared to distal print however, once retrieved from the scene of crime allow the identification of the individual who committed the crime. Therefore, this case report presents a burglary case where phalangeal prints from the finger retrieved from the site of crime matched the prints collected from the suspect. This case report also goes through advancements brought by Automated Biometric Identification System (ABIS) such as enhancing low-quality latent fingerprint, real-time picture and matching, and integration of other biometric systems such as Iris scan, Facial recognition.
11
Basila, A., and A. Danladi. "Design, simulate and construct a fingerprints attendance system with data logging." Nigerian Journal of Technology 40, no. 4 (2021): 703–12. http://dx.doi.org/10.4314/njt.v40i4.17.
Full textAbstract:

 
 
 Because of its uniqueness and accuracy over time, fingerprint has been used for identification for many years, more recently being automated due to advancement in computing capabilities, fingerprint identification is one of the most well-known and popular biometric identification systems. The methodology comprised of a power supply, input voltage to the LM7805 should be at least 2v greater than the required 5v output according to its rating from the datasheet; hence it requires an input voltage of atleast 7v. Hence, 9v battery was adopted with current rating of 1A for convenience. The LM7805 voltage regulator IC was used since we needed 5v. The Fingerprints Attendance System with Data-login was designed, simulated and implemented/constructed, and was able to address the attendance issues in Adamawa State University, Mubi by the following means: no time waste as the attendance is taken during lecture without intervention of the lecturer, managing the attendance is automated, no chance for buddy signing, real time attendance capture, evaluate level of attendance for students automatically. Also, this system can easily be applied for examination attendance and monitoring.
 
 
12
Fatamatuz, Ayasa Khan. "Improvement of Biometric Authentication System Applying Fingerprint." International Journal of Engineering Research & Science 4, no. 12 (2018): 19–26. https://doi.org/10.5281/zenodo.2529363.
Full textAbstract:
<strong><em>Abstract</em></strong><strong>—</strong><em>.</em><em>The biometric system plays an important role in everyone life. To identify one identity, the finger is one of many forms of the biometrics are generally used. The fingerprint is the verified function to identify a match between two person’s fingerprints. </em><em>Here a simple and effective system for biometric fingerprint based voter identity system has been proposed that is based on image enhancement and correct minutiae extraction. Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching. In this research a fast fingerprint enhancement and minutiae extraction algorithm have been presented which improve the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracting correct minutiae.</em>
13
Jarold Kinsman B. Alagos. "Issues and Challenges Encountered by Police Officers in Automated Fingerprint Identification System Operation." Journal of Electrical Systems 20, no. 5s (2024): 1589–99. http://dx.doi.org/10.52783/jes.2492.
Full textAbstract:
Automated Fingerprint Identification Systems (AFIS) play a vital role in modern law enforcement by aiding in the identification and apprehension of criminals through fingerprint analysis. However, despite their significance, challenges in operating AFIS remain the main hindrance to its effective utilization. Thus, this study aims to investigate the issues and challenges faced by police officers in the Philippines with AFIS. Employing a convergent parallel mixed methods research design, quantitative data were collected through a survey questionnaire, while qualitative data were obtained through in-depth interviews. The study participants comprised six experienced Filipino police officers purposively chosen for their exposure to AFIS operations. The study uses the mixed method. Descriptive statistics and Mann-Whitney U Test were used during quantitative data analysis, while thematic analysis was used in analyzing qualitative data. The study findings revealed that the top challenge faced by police officers in the Philippines is comparing and storing fingerprints in the AFIS database, primarily due to insufficient training and technical support. Themes such as regular training, technical support, and continuous learning emerged to address the challenges related to fingerprint quality, technical issues, storage limitations, and hardware problems. The study also revealed that Filipino police officers encounter moderate challenges when operating AFIS, with variations depending on sex, age, and length of service. In conclusion, the study underscores the significance of continuous training, technical support, and effective communication for successful AFIS operations. Law enforcement agencies are urged to prioritize providing proper training and technical support to ensure accurate and reliable utilization of this technology in policing.
14
Odu, Tiwalade O., Moses O. Olaniyan, Tokunbo Ogunfunmi, Isaac A. Samuel, Joke A. Badejo, and Aderemi A. Atayero. "Multi-Instance Contingent Fusion for the Verification of Infant Fingerprints." Journal of Electrical and Computer Engineering 2024 (January 2, 2024): 1–10. http://dx.doi.org/10.1155/2024/7728707.
Full textAbstract:
It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints.
15
Aguilar, Gualberto, Gabriel Sánchez, Karina Toscano, Mariko Nakano, and Héctor Pérez. "Fingerprint Recognition Using Local Features." Revista Facultad de Ingeniería Universidad de Antioquia, no. 46 (December 11, 2013): 101–9. http://dx.doi.org/10.17533/udea.redin.17933.
Full textAbstract:
Fingerprint recognition is one of the most popular methods used for identification with greater degree of success. The fingerprint has unique characteristics called minutiae, which are points where a curve track finishes, intersect or branches off. Identification systems using fingerprints biometric patterns are called AFIS (Automatic Fingerprint Identification System). In this work a method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters for image enhancement and later a novel method of recognition using local features.
16
Sreeramana, Aithal, and Prasad K. Krishna. "A Critical Study on Fingerprint Image Sensing and Acquisition Technology." International Journal of Case Studies in Business, IT and Education (IJCSBE) 1, no. 2 (2017): 86–92. https://doi.org/10.5281/zenodo.1130581.
Full textAbstract:
Automatic Fingerprint Recognition System (AFIS) mainly depends on the quality of the fingerprint captured during the enrollment process, even though a lot of techniques developed in literature for fingerprint matching, all most all system is influenced or affected by the quality of acquisition method. Automated fingerprint identification system requires fingerprint images in a special format. Normally it can't receive and process the photographic image or photo taken from virtual camera or cell camera. There are many special acquisition or sensing strategies to extract the ridge-and-valley structure of finger skin or fingerprint. Traditionally, in law or regulation enforcement packages, fingerprints were especially received offline. Fingerprint acquisition can be specially classified into groups as an offline and live scan. An offline acquisition technique gets input through inked affect of the fingertip on paper and digitized with the aid of the paper with an optical scanner or video digital camera. The live acquisition is received through the sensor that is having the ability to directly digitize the sensing tip of the finger. As the fingerprint sensing, image processing, signal processing, and communication technology advance, an increasing number of new technologies in this acquisition technology are arriving at the main facet. In this paper, we discuss different types of fingerprint acquisition technologies, which involve optical, ultrasonic, capacitance, passive capacitance, and active capacitance. This paper helps to identify new fingerprint acquisition technology.
17
Le, Ngoc Tuyen, Duc Huy Le, Jing-Wein Wang, and Chih-Chiang Wang. "Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection." Entropy 21, no. 8 (2019): 786. http://dx.doi.org/10.3390/e21080786.
Full textAbstract:
Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.
18
Rivandi, Pranoko, Astuti Winda, Dewanto Satrio, and Mahmud Iwan Solihin. "Automotive Start–Stop Engine Based on Fingerprint Recognition System." E3S Web of Conferences 130 (2019): 01022. http://dx.doi.org/10.1051/e3sconf/201913001022.
Full textAbstract:
Automated vehicle security system plays an important rule in nowadays advance automotive technology. One of the methods which can be applied for a security system is based on biometric identification system. Fingerprint recognition is one of the biometric systems that can be applied to the security system. In this work, fingerprint recognition system to start the motorcycle engine is developed. The fingerprint of the owner and other authorized persons will be stored into the database, then while the time of starting the engine of the vehicle, the fingerprint will be validated with the database. The minutiae extraction method is applied to find the difference of fingerprint each other after turn the image into grayscale and thinning. After the extraction, the next step is finding the ridge edge and bifurcation. The result of the image will be used as input to the Artificial Neural Network (ANN) to recognize authorized person only. The experiment of fingerprint recognition system shows that automatic start-stop engine using fingerprint recognition system based minutiae extraction and Artificial Neural Network (ANN) has accuracy 100 % and 100 %, respectively.
19
A, Jagadamba. "Forensic Fingerprint Analysis." International Journal of Innovative Research in Information Security 10, no. 03 (2024): 386–91. http://dx.doi.org/10.26562/ijiris.2024.v1003.42.
Full textAbstract:
Fingerprint evidence found at crime scenes provides vital impressions left when these skin secretions touch surfaces clues in serial criminal investigations. A fingerprint identification system employing deep machine learning and Convolutional Neural Networks (CNNs) could automate the analysis process. Images obtained from various physical and chemical crime scene investiga2tion techniques are entered into the database. However, partial latent prints lifted from scenes are often difficult to classify. The system operates in three phases: preprocessing fingerprint images, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points - ridge endings and bifurcations. False minutiae removal further refines the data. The preprocessed fingerprint data serves as input to train and test the CNN model. As the system persist due to the immutable individuality of fingerprint ridge arrangements [5]. While criminals attempt concealment, fingerprint traces stubbornly remain where other evidence would dissipate [6]. Without these durable biometric markers, crime scenes would often lack the critical traces needed to connect acts to perpetrators [7]. Latent prints lifted from crime scenes first undergo photographic documentation and chemical enhancement techniques in order to visualize trace details [8]. Computer analysis then further improves clarity, isolating minute identifying features known as minutiae [9]. Algorithmic extraction of differentiating traits classifies new latent prints, it continuously incorporates the prints enables training of automated comparison systems using along with confirmed suspect identity matches to improve accuracy. Automated classification and matching facilitate identification. The approach scales as the database grows in size without proportionate growth in human effort. Rapid fingerprint evidence analysis accelerates investigations, potentially solving more crimes by linking serial cases through a central digital repository. I aimed to restate the key technical ideas and flow using alternative vocabulary and phrasing while preserving semantic meaning. Please let me know if you need any clarification or have additional requirements for rephrasing the passage.
20
Priati, Assiroj, L. H. S. Warnars H., Abdurrachman E., I. Kistijantoro A., and Doucet A. "Measuring memetic algorithm performance on image fingerprints dataset." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 96~104. https://doi.org/10.12928/TELKOMNIKA.v19i1.16418.
Full textAbstract:
Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.
21
Debnath, Asutosh. "DACTYLOGRAPHY: The Scientific Study of Fingerprint." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 1388–91. http://dx.doi.org/10.22214/ijraset.2024.64129.
Full textAbstract:
Fingerprint analysis has become important because of its reliability and scientific explanation in the courts. Fingerprint has three principles classification, uniqueness and identification. Now there are digital biometric scanner has the ability to access by fingerprint within a minute. This Automated Fingerprint Identification System has made easier to accessible. But the reliability would be little less than other ways to identify fingerprints. Because due to the malfunction of the scanner database can be manipulated or hacked by anyone to access the security. There are various types of fingerprints which needed to know for analyzing it. These types are 1. Patent Fingerprint(visible) 2. Latent Fingerprint(invisible) 3. Plastic/3d; to developing fingerprints there are different methods for different types of fingerprint. Fingerprint experts tend to develop fingerprint in crime scene for solving the crime. Whereas fingerprint become most important physical evidence in matter of solving crime. For developing to analyzing fingerprint expertise would be necessary for establishment. But there are various challenges for forensic experts which are distort prints, smudge prints, contaminated crime scene, database error, partial prints, etc. for the recording of the fingerprint on the wall also be challenging for experts which only can be recorded by photography. Henry classification system would be time consuming to evaluate the recorded fingerprints. Then there is chemical method to develop fingerprint due to the secretion of sweat, oil and other body fluids which make an invisible print. The outermost skin layers have various pores through where body fluid tend to secrets which is responsible for making fingerprints. To make it visible various chemical need to apply so that the secreted body fluid will react with chemical and its visibility will appear. There are some pattern types which tend to administer the individuality such as Arch, Loop and Whorl. Every pattern in itself is unique which can’t be match with any other person. Another way is ridge characteristics which is also valuable way to identify fingerprint. Because of its unique characteristics which tend to differ for every person and not only one characteristics but also 14 ridge characteristics are possible to identify through fingerprint. Porescopy and Edgescopy these are other ways to identify fingerprints but it is not practicable in India right now. There are basic two ways to develop latent fingerprints powder method and chemical methods. Through powder method, various powder and brushes will be need to develop fingerprints but it need to be know the surface where fingerprints are found. In chemical method there would be various chemical compound which needed to apply for the development of the latent fingerprints
22
Joy, Reji, and Hemalatha S. "A Gradient Based Approach for Fingerprint Image Segmentation using Morphological Operators." International Journal of Engineering & Technology 7, no. 4 (2018): 2453. http://dx.doi.org/10.14419/ijet.v7i4.16244.
Full textAbstract:
The advancement of science and technology has made the reliable individual recognition and identification systems to become very popular. From the various biometric characteristics, fingerprint is one of the popular method because of its easiness and not much effort is required to acquire fingerprint. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. During fingerprint segmentation process the input image is decomposed into foreground and background areas. The foreground area contains information that are needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false features. So in an AFIS, fingerprint image segmentation plays an important role in carefully separating ridge like part (foreground) from noisy background. Gradient based method is commonly used for segmentation process. Since gradient estimation is erroneous in noisy images, the study proposes a combination of gradient mask and morphological operations to segment fingerprint foreground effectively. The results obtained prove that the new method is suited for fingerprint segmentation.
23
Alsharman, Nesreen, Adeeb Saaidah, Omar Almomani, Ibrahim Jawarneh, and Laila Al-Qaisi. "Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection." Security and Communication Networks 2022 (April 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/4375232.
Full textAbstract:
Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.
24
Kot, Edyta. "Development of a technologically advanced IT system enabling automated processing of information collected in forensic biometric databases in order to combat crime or identify people." Issues of Forensic Science 308 (2020): 69–74. http://dx.doi.org/10.34836/pk.2020.308.5.
Full textAbstract:
Forensic biometric databases are an out of court tool to support the work of law enforcement agencies. They are used to detect the perpetrators of crimes, indicate the connections of a person with previously committed crimes, and allow the identification of living persons and corpses with unknown identity or individuals trying to hide their identity. In police practice, among the methods of identification of living persons and corpses, fingerprint and DNA tests are mainly used. These two forensic areas are supported by such tools such as AFIS (Automatic Fingerprint Identification System) and CODIS (Combined DNA Index System). These are the main police systems operating within two datasets, namely the dactyloscopic dataset and the DNA dataset. The systems are operating in two different locations of the Central Forensic Laboratory of the Police (CFLP). This results in the processing of individual biometric data independently of each other at different times and locations (Fingerprint Examination Department and Biology Department). Currently, there is no comprehensive approach to the collection and processing of biometric data such as fingerprints, DNA or facial images. To improve the exchange of information between police authorities, in July 2019, the Central Forensic Laboratory of the Police submitted a project application, and on 25 November 2019 signed with the National Center for Research and Development the grant agreement no. DOB – BIO10/09/01/2019 for the implementation and financing of the project in the area of state defense and security (competition no. 10/2019) entitled “Development of a technologically advanced information system enabling automated processing of information collected in forensic biometric databases for the purpose of combating crime or identifying people – acronym “BIOMETRIA”.
25
Lourdes, Reyes Santos. "Automated Fingerprint Identification System: Basis for Modernization of the Identification and Records Division (IRD)." International Journal of Recent Innovations in Academic Research 8, no. 8 (2024): 37–48. https://doi.org/10.5281/zenodo.13462875.
Full textAbstract:
The significance of fostering peace and order for sustainable development is crucial, creating an environment conducive to growth. Research in law enforcement highlights the technology gap faced by developing nations dealing with terrorism, criminal activities, and transparency challenges. While Automated Fingerprint Identification Systems (AFIS) are globally recognized for their transformative impact on criminal identification, their implementation at the National Bureau of Investigation (NBI) in the Philippines lags behind the Philippine National Police's (PNP) adoption. This study focuses on automating the NBI's fingerprint identification system to modernize the Identification and Records Division (IRD) through an explanatory sequential mixed method design, combining quantitative and qualitative analyses with a focus on IRD employees. The assessment reveals the potential of AFIS in enhancing the NBI's identification processes, database system management, and information accuracy, with overall mean scores of 4.57 for the computerized identification process, 4.56 for the computerized database system, and 4.56 for the accuracy and reliability of information, all indicating strong agreement. While AFIS offers benefits such as error reduction and improved crime-solving capabilities, challenges like image quality dependency and high implementation costs require attention. The proposed action plan, with an overall mean score of 4.66 (highly recommended), emphasizes budget allocation, IRD personnel training, and high-quality fingerprint scanning techniques to facilitate successful AFIS integration. In conclusion, the study underscores the potential benefits of AFIS in modernizing the NBI's identification processes, addressing challenges, and enhancing efficiency in law enforcement operations. The proposed action plan, supported by high mean scores, provides a structured framework for successfully implementing AFIS. Recommendations for continuous training, strategic planning, and methodical execution aim to propel the modernization of the NBI's identification and records division, ultimately improving criminal investigation and clearance issuance capabilities.
26
Lourdes, Reyes Santos. "Automated Fingerprint Identification System: Basis for Modernization of the Identification and Records Division (IRD)." International Journal of Recent Innovations in Academic Research 8, no. 8 (2024): 37–48. https://doi.org/10.5281/zenodo.13462875.
Full textAbstract:
The significance of fostering peace and order for sustainable development is crucial, creating an environment conducive to growth. Research in law enforcement highlights the technology gap faced by developing nations dealing with terrorism, criminal activities, and transparency challenges. While Automated Fingerprint Identification Systems (AFIS) are globally recognized for their transformative impact on criminal identification, their implementation at the National Bureau of Investigation (NBI) in the Philippines lags behind the Philippine National Police's (PNP) adoption. This study focuses on automating the NBI's fingerprint identification system to modernize the Identification and Records Division (IRD) through an explanatory sequential mixed method design, combining quantitative and qualitative analyses with a focus on IRD employees. The assessment reveals the potential of AFIS in enhancing the NBI's identification processes, database system management, and information accuracy, with overall mean scores of 4.57 for the computerized identification process, 4.56 for the computerized database system, and 4.56 for the accuracy and reliability of information, all indicating strong agreement. While AFIS offers benefits such as error reduction and improved crime-solving capabilities, challenges like image quality dependency and high implementation costs require attention. The proposed action plan, with an overall mean score of 4.66 (highly recommended), emphasizes budget allocation, IRD personnel training, and high-quality fingerprint scanning techniques to facilitate successful AFIS integration. In conclusion, the study underscores the potential benefits of AFIS in modernizing the NBI's identification processes, addressing challenges, and enhancing efficiency in law enforcement operations. The proposed action plan, supported by high mean scores, provides a structured framework for successfully implementing AFIS. Recommendations for continuous training, strategic planning, and methodical execution aim to propel the modernization of the NBI's identification and records division, ultimately improving criminal investigation and clearance issuance capabilities.
27
Marák, Pavol, and Alexander Hambalík. "Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation." Tatra Mountains Mathematical Publications 67, no. 1 (2016): 117–34. http://dx.doi.org/10.1515/tmmp-2016-0035.
Full textAbstract:
Abstract Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable results. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demonstrated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.
28
Obansola, Oluwatoyin Yemi, Oladayo Ezekiel Makinde, Adesoji Henry Adeshina, and Oyeronke Bunmi Adebayo. "Development of Staff Attendance Management System using Fingerprint Biometric Identification Technique." Greener Journal of Science, Engineering and Technological Research 6, no. 3 (2016): 55–69. https://doi.org/10.15580/GJSETR.2016.3.101916185.
Full textAbstract:
Attendance management is the act of managing attendance or presence in a work setting. Considering an academic institute, the conventional method of taking staff attendance is by signing on paper is very time consuming and insecure, hence inefficient. Maintaining these records for a long time adds to the difficulty of the task. In lieu of this, an efficient system is proposed to solve the problem of manual attendance. In this research work, staff attendance is taken electronically with the help of a fingerprint Biometric system, and all the records are saved for subsequent operations. Fingerprints are considered to be the best and fastest method for biometric identification because they are secure to use, unique for every person and do not change in one's lifetime. Staff attendance management system using fingerprint biometric identification technique employs an automated system to calculate staff attendance in lecture rooms in an institution and do further calculations of daily and monthly attendance summary in order to reduce human errors during calculations. In essence, this system can be employed in curbing the problems of tardiness or lateness of lecturers to lecture rooms, impersonation and missing lecture periods in any institution. The system will also improve the productivity of any institution if properly implemented.
29
Usman, M., M.A. Obomeghie, S. Ezolome, K. Abu, and A.O. Usman. "Development of a Radio Frequency Identification Attendance System." Journal of Scientific and Engineering Research 10, no. 5 (2023): 172–80. https://doi.org/10.5281/zenodo.10458448.
Full textAbstract:
<strong>Abstract </strong>Attendance systems are used to track student involvement in lectures and other academic activities, which are important in educational institutions. The manual procedures for taking attendance take a lot of time and are vulnerable to student imitation. The goal of this project, dubbed "RFID Attendance System," is to develop and build an automated attendance system that includes a fingerprint detector. This system includes a fingerprint sensor, tags, an RFID reader, a microcontroller, and an RTC module. The RTC module keeps track of the precise moment a student entered and exited, while the RFID reader reads RFID cards and transmits the information to the microcontroller unit for processing. The system's results are astounding because the card reader was able to read the data on the card that was placed next to it in just 5 to 20 seconds.
30
Oladimeji, Ismaila W., Omidiora E. Olusayo, Ismaila Folasade M., and Falohun Adeleye S.. "Multi-Level Access Control System in Automated Teller Machines." International Journal of Computer Science and Mobile Computing 10, no. 4 (2021): 146–55. http://dx.doi.org/10.47760/ijcsmc.2021.v10i04.020.
Full textAbstract:
E-commerce theft involves using lost/stolen debit/credit cards, forging checks, misleading accounting practices, etc. Due to carelessness of cardholders and criminality activities of fraudsters, the personal identification number (PIN) and using account level based fraud detection techniques methods are inadequate to cub the activities of fraudsters. In recent times, researchers have made efforts of improving cyber-security by employing biometrics traits based security system for authentication. This paper proposed a multi-level fraud detection system in automated teller machine (ATM) operations. The system included PIN level, account-level and biometric level. Acquired RealScan-F scanner was used to capture liveness fingers. Transactional data were generated for each individual fingerprint with unique PIN. The results of the simulation showed that (i) the classification at account level only yielded averages 84.3% precision, 94.5% accuracy and 5.25% false alarm rate; (ii) matching at biometric level using liveness fingerprints samples yielded 0% APCER , 0% NPCER and 100% accuracy better than using fingerprints samples that produced 4.25% APCER , 2.33% NPCER and 93.42% accuracy; (iii) combining the three levels with the condition that all the levels must be positive produced 87.5% precision,84.9% accuracy and 2.65% false alarm rate; (iv) while the classification using voting technique yielded 99.15% precision, 97.35% accuracy and 0.47% false alarm.
31
Hussein, Enas. "Fingerprint Identification using Multiwavelet Transform." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 131–38. http://dx.doi.org/10.55562/jrucs.v23i2.485.
Full textAbstract:
identification systems have been developed to achieve automatic identification of a person based on his physiological or behavioral characteristics. Biometric systems are critical in a wide range of applications such as banking system, E-commerce, smart cards, and access control to secure system. Automatic fingerprint identification is one of the most reliable biometric system, which is used for identifying persons. This study, aim is to design a fingerprint identification system, which is capable of identifying a fingerprint with high level of accuracy. Therefore, this system can be applied to a wide range of forensic applications. The proposed algorithm based upon multiwavelet transform (one level) as feature extraction and minimum distance classifier ( Euclidean distance) to make automatic fingerprint identification . The identification accuracy of this algorithm has been found to be 72%.
32
King, Bernard S. Saul, B. Saul Joy, and T. Soberano Kristine. "Utilizing Convolutional Neural Networks for Fingerprint-Based Attendance Monitoring." International Journal of Multidisciplinary Research and Analysis 06, no. 04 (2023): 1343–51. https://doi.org/10.5281/zenodo.7801690.
Full textAbstract:
The traditional method of taking attendance using paper sheets is prone to errors like impersonation, loss, or theft. To solve this issue, automatic attendance systems utilizing identification technology such as barcode badges, electronic tags, touch screens, magnetic stripe cards, and biometrics have been implemented. Biometric technology uses physiological or behavioral characteristics for identification purposes, but traditional biometric systems have limitations such as vulnerability to damage or alteration over time, and variations in occlusions, poses, facial expressions, and illumination can affect face recognition accuracy. Fingerprint identification relies on the distinctiveness of fingerprints and involves comparing two impressions of the friction ridges on human fingers or toes to determine if they belong to the same individual. There are five primary categories of fingerprints: arch, tented arch, left loop, right loop, and whorl. Various algorithms have been developed to recognize fingerprints using minutiaebased matching, which involves identifying key features like ridge ending and bifurcation. Deep learning algorithms, particularly convolutional neural networks, have been successful in improving identification accuracy by extracting features automatically from fingerprint images. In recent times, securing personal data has become increasingly important, and the Convolutional Neural Network (CNN) identification system is recommended for improving accuracy and performance. This paper proposes a fingerprint identification system that combines three models: CNN, Softmax, and Random Forest (RF) classifiers. The conventional system uses K-means and DBSCAN algorithms to separate the foreground and background regions and extracts features using CNNs and dropout approach. The Softmax acts as a recognizer. The proposed algorithm is evaluated on a public database and shows promising results, providing an accurate and efficient biometric identification system.
33
Effiong, Otobong J., Akaninyene B. Obot, Kingsley M. Udofia, and Kufre M. Udofia. "Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation." Journal of Engineering Research and Reports 26, no. 10 (2024): 186–98. http://dx.doi.org/10.9734/jerr/2024/v26i101298.
Full textAbstract:
This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.
34
Zhang, Zhen, and Li Liu. "The Research of Algorithms for Fingerprint Characteristic Extraction and Matching." Advanced Materials Research 433-440 (January 2012): 3479–82. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3479.
Full textAbstract:
Fingerprint recognition plays an important role in identification of organism characters. Automatic fingerprint identification system(AFIS)is a technology based on computer or microprocessor with advantages of convenience and high efficiency. The extraction and matching of fingerprint minutiae is a necessary step in automatic fingerprint recognition system. A set of algorithms for minutiae extraction and minutiae matching of fingerprint image are proposed in this paper based on the analysis of the inherent minutiae of fingerprint.
35
M., Naga Triveni. "Recognition of Finger mark using CNN." IJRSET JANUARY Volume 10 Issue 1 10, no. 1 (2023): 7–9. https://doi.org/10.5281/zenodo.8434408.
Full textAbstract:
In present-days, the technological development in the field of data collection, processing, storing along with the field of research in pattern recognition, machine learning and deep learning serves abiometric person recognition processing fingerprint. In this work, the proposed model is a classificationsystem to recognize and match images of fingerprints. ACNN architecture is used to develop a model for detection. The present study uses approach to ensure the performance of the system. Finger print recognition system used for identifies the entity who involved in the database helps to automate fingerprint identification process. Preprocessing was performed with fingerprint thinning and minutiae extractionwithmethod. Feature extraction will be done by the CNN classifier.
36
Botani, Dilshad, and Wasfi Saalih. "Automatic Fingerprint Identification System Using Robust Distance." TANMIYAT AL-RAFIDAIN 28, no. 83 (2006): 27–38. http://dx.doi.org/10.33899/tanra.2006.161638.
Full text
37
H. Harisha, M. Reshma, M. Shivani, and N. Michael Franklin. "Automatic Biometric Fingerprint Sanitization System." Asian Journal of Applied Science and Technology 07, no. 02 (2023): 84–92. http://dx.doi.org/10.38177/ajast.2023.7211.
Full textAbstract:
Fingerprint recognition is a safe and convenient biometric identification method that is increasingly being used for welfare program users. Many organizations employ fingerprint scanners to identify beneficiaries. The COVID-19 outbreak has jeopardized fingerprint authentication security. A group of people touching the sensors can result in viral transmission. COVID-19 can persist for at least 5 days on typical surfaces such as wood, plastic, metal, and glass, according to research. Despite all normal operational practices, shared public areas such as Ration Shops, attendance purpose in IT sectors and hospitals, etc... have raised the risk of virus transmission. In this context, the current study attempts to establish a safe and healthy environment for individuals by utilizing a UVC-based self-sanitizing technology for fingerprint scanners.
38
Gu, Yaomin, Shengjun Sun, Lan Wang, Ping Gu, and Yuankai Li. "Key technology research for mobile police terminal fingerprint collection for quick comparison using automated fingerprint identification system." Journal of Forensic Science and Medicine 5, no. 1 (2019): 57. http://dx.doi.org/10.4103/jfsm.jfsm_36_18.
Full text
39
Egli Anthonioz, N. M., and C. Champod. "Evidence evaluation in fingerprint comparison and automated fingerprint identification systems—Modeling between finger variability." Forensic Science International 235 (February 2014): 86–101. http://dx.doi.org/10.1016/j.forsciint.2013.12.003.
Full text
40
Egli, Nicole M., Christophe Champod, and Pierre Margot. "Evidence evaluation in fingerprint comparison and automated fingerprint identification systems—Modelling within finger variability." Forensic Science International 167, no. 2-3 (2007): 189–95. http://dx.doi.org/10.1016/j.forsciint.2006.06.054.
Full text
41
Elohor, Oyinloye Oghenerukevwe, Akinbohun Folake, Thompson Aderonke, and Korede Bashir. "Biometric, Fingervein, Machine L Implementation of a Security System, Using Captured Fingervein, Applying the Concepts of Machine Learning." International Journal of Engineering and Computer Science 9, no. 04 (2020): 24994–5007. http://dx.doi.org/10.18535/ijecs/v9i04.4470.
Full textAbstract:
This work explores the field of biometric finger vein recognition – which is the identification of individuals using the unique vein patterns under their finger skins. This work also includes the implementation of an Android fingerprint biometric system using the Android Near InfraRed (NIR) module, which exists to show the similarities and differences between the two (fingervein and fingerprint) prevalent biometric features. This work thus confirms that finger vein recognition shows great promise as an accurate solution to modern society’s problem of automated personal authentication
42
Yuan, Ren Min. "The Research and Implementation about AFIS." Applied Mechanics and Materials 341-342 (July 2013): 830–33. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.830.
Full textAbstract:
Automatic identification technology becomes a urgent need of production and life, authentication technology gained worldwide attention because of its high reliability, fingerprint identification technology which applied to social security system can accurately determine protects a person's identity and prevent the phenomenon of the pension of falsely claim that solves this one long-term puzzling problem. Based on the preeminent fingerprint identification algorithm is improved, and combining with actual demand the improved algorithm, Using the fingerprint identification and IC card combination way realization of distributed fingerprint authentication system collection. Through the practical efficiency test analysis proves the whole system is feasible, and the running effect is good by practical application.
43
Patil, Akhil, Aditya Sakhare, Amiruddin Samani, and Mahesh Prajapati. "Digital Forensics based Defence Security System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2695–700. http://dx.doi.org/10.22214/ijraset.2022.41883.
Full textAbstract:
Abstract: This project addresses the structure, implementation and application of digital forensics security System with the assistance of face recognition and fingerprint identification which might be employed in Military Bases and other defence bases. Forensics munition using face and fingerprint recognition may be a system during which tracking of daily activity of officers is in an automatic manner and done by the program itself which is finished by the popularity and identification of the face with the assistance of a camera module which is connected with the system. The camera module helps the system with the initial a part of the program which is initialization of the camera on which the system can perform its operation. When the face comes before of the camera and provides their fingerprints, the program compares the face within the camera with the photographs and detect the fingerprint within the database and recognizes it. These image data are basically known data for the system and is that the data on which the system is trained. The system, if recognizes the face after the comparison with the database, displays the name of the person’s face as an alternative displays “UNKNOWN” if the faces don’t match. Then it tracks the identity of the person file of only the matched faces. This data is stored in an excel file format safely
44
Lee, Beom-Oh, and Sang-Hun Lee. "A Study on the Development of the Automatic Fingerprint Search System : Interpol's Automatic Fingerprint Search System (AFIS) Interoperability Plan." Korean Society of Private Security 23, no. 2 (2024): 297–317. http://dx.doi.org/10.56603/jksps.2024.23.2.297.
Full textAbstract:
International Criminal Police Organization's (Interpol) Automatic Fingerprinting System (AFIS) enables rapid and accurate identification using vast amounts of fingerprint data collected worldwide, and the system plays an important role in various fields, including identification of international criminals and terrorists, search for missing persons, and recovery of stolen and missing property. In addition, AFIS supports law enforcement agencies in each country to work together to effectively solve crimes, which is acting as an essential tool to counter the rise of international crimes. This study aims to analyze in-depth the operation method of the International Criminal Police Organization (Interpol)'s automatic fingerprint search system (AFIS) and its use cases, and through this, the importance of the automatic fingerprint search system (AFIS) in international criminal investigations and its effectiveness are highlighted. In addition, along with the technological advancement of the automatic fingerprint search system (AFIS), the latest trends and challenges to increase the efficiency of the system will be examined. Through this, it is intended to contribute to the search for application plans and future development directions in Korea.
45
Esekhaigbe, Emmanuel, and Emmanuel O. Okoduwa. "Design and implementation of a fingerprint-based biometric access control system." Journal of Advances in Science and Engineering 7, no. 1 (2022): 18–23. http://dx.doi.org/10.37121/jase.v7i1.183.
Full textAbstract:
Security systems are often penetrated by sophisticated criminals, thus there is always a need for new solutions to be devised to give sufficient security to houses and other locations. The goal of this project is to build and deploy a fingerprint-based biometric access control system. The fingerprint is a pattern of ridges and valleys on the surface of a fingertip. Among various biometrics, fingerprint recognition is the most extensively and internationally accepted biometric because of its uniqueness, accuracy, cost-effectiveness, non-transferability, and ease of use. Presented is the system architecture for the system development that demonstrates component augmentation, detail extraction, and matching methodologies. MATLAB and the programming language C were used to develop a software application that was used to build algorithms for improvement, minutiae extraction, and matching processing. The software works by extracting meaningful features known as minutiae points from the person’s fingerprint, then records and stores these minutiae points to verify the person’s identity in the future. The resulting minutiae information is used to find matching fingerprints and to register these fingerprints in the system database. Finally, a verification system and identification system were realized. The proposed automatic door access control system was implemented using the Arduino Atmega 328p microcontroller. The proposed system was tried-out in real-time, and its performance was deemed adequate.
46
Wan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, and Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net." Sensors 20, no. 16 (2020): 4473. http://dx.doi.org/10.3390/s20164473.
Full textAbstract:
Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.
47
Krzemińska, Beata. "AFIS system in the Polish Police – yesterday, today, tomorrow." Issues of Forensic Science 300 (2018): 76–86. http://dx.doi.org/10.34836/pk.2018.300.5.
Full textAbstract:
The Automated Fingerprint Identification System (AFIS) has been in operating in Poland for 18 years. During this period, it has undergone many modernizations and transformations. The article presents its evolution in the context of changes taking place in the European Union. This applies both to the legislative developments in the area of freedom, security and justice as well as to the technical solutions being implemented. An attempt was also made to forecast the possibility of further development of AFIS from the perspective of large-scale systems using the latest biometric mechanisms available on the IT market.
48
A. Ragmac, Arlan Raymark, and Elmie A. Allanic. "Enhancing Fingerprint System: A Case Study on Bridging Gaps to Improve Efficiency, Standardization and Public Trust." Mediterranean Journal of Basic and Applied Sciences 09, no. 02 (2025): 166–79. https://doi.org/10.46382/mjbas.2025.9215.
Full textAbstract:
Fingerprint identification remains a cornerstone of modern forensic science, yet systemic challenges hinder its optimal use in various regions. This study aimed to investigate operational, technical, and human factors affecting the implementation of the Automated Fingerprint Identification System AFIS and to identify strategies for improvement within Region 13 (Caraga), Philippines. Employing a qualitative case study design, the research was conducted at the Regional Forensic Unit 13, the primary forensic facility in the region. The study involved twenty (20) purposively selected fingerprint examiners and SOCO investigators currently assigned to the unit. Data collection was carried out using semi-structured interviews guided by a validated forensic-specific interview tool. Thematic analysis was applied to the data to uncover core patterns and gaps in system utilization. The study revealed six key themes: Print Quality and Processing Challenges, Interoperability and Standardization Issues in Fingerprint Systems, Bridging Practice, System, and Coordination Gaps, Strengthening AFIS through Upgrading Systems, Enhancing Training, and Standardizing Processes, Enhancing Investigative Efficiency and Judicial Outcomes, and Promoting Interagency Integration and Public Trust. These findings highlight general limitations that reduce the effectiveness of fingerprint systems and offer a basis for holistic reform. In conclusion, improving AFIS requires a coordinated national strategy integrating technical upgrades, capacity building, and procedural standardization. It may be recommended that policymakers develop and implement an interagency framework to support forensic modernization and foster greater public trust in justice systems.
49
SJ, Dilmini, Mandula S.P. S, Rajapaksha R.A.T.M, Delgasdeniya D.D. G, Erandika Lakmali, and Pradeepa Bandara. "CRIMINAL INVESTIGATION TRACKER WITH SUSPECT PREDICTION USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 7, no. 9 (2023): 34–39. http://dx.doi.org/10.33564/ijeast.2023.v07i09.006.
Full textAbstract:
An automated approach to identifying offenders in Sri Lanka would be better than the current system. Obtaining information from eyewitnesses is one of the less reliable approaches and procedures still in use today. Automated criminal identification has the ability to save lives, notwithstanding Sri Lankan culture's lack of awareness of the issue. Using cutting-edge technology like biometrics to finish this task would be the most accurate strategy. The most notable outcomes will be obtained by applying fingerprint and face recognition as biometric techniques. The main responsibilities will be image optimization and criminality. CCTV footage may be used to identify a person's fingerprint, identify a person's face, and identify crimes involving weapons. Additionally, we unveil a notification system and condense the police report to Additionally, to make it simpler for police officers to understand the essential points of the crime, we develop a notification system and condense the police report. Additionally, if an incident involving a weapon is detected, an automated notice of the crime with all the relevant facts is sent to the closest police station. The summarization of the police report is what makes this the most original. In order to improve the efficacy of the overall image, the system will quickly and precisely identify the full crime scene, identify, and recognize the suspects using their faces and fingerprints, and detect firearms. This study provides a novel approach for crime prediction based on real-world data, and criminality incorporation. A crime or occurrence should be reported to the appropriate agencies, and the suggested web application should be improved further to offer a workable channel of communication.
50
Jiang, Bin, Yikun Zhao, Hongmei Yi, et al. "PIDS: A User-Friendly Plant DNA Fingerprint Database Management System." Genes 11, no. 4 (2020): 373. http://dx.doi.org/10.3390/genes11040373.
Full textAbstract:
The high variability and somatic stability of DNA fingerprints can be used to identify individuals, which is of great value in plant breeding. DNA fingerprint databases are essential and important tools for plant molecular research because they provide powerful technical and information support for crop breeding, variety quality control, variety right protection, and molecular marker-assisted breeding. Building a DNA fingerprint database involves the production of large amounts of heterogeneous data for which storage, analysis, and retrieval are time and resource consuming. To process the large amounts of data generated by laboratories and conduct quality control, a database management system is urgently needed to track samples and analyze data. We developed the plant international DNA-fingerprinting system (PIDS) using an open source web server and free software that has automatic collection, storage, and efficient management functions based on merging and comparison algorithms to handle massive microsatellite DNA fingerprint data. PIDS also can perform genetic analyses. This system can match a corresponding capillary electrophoresis image on each primer locus as fingerprint data to upload to the server. PIDS provides free customization and extension of back-end functions to meet the requirements of different laboratories. This system can be a significant tool for plant breeders and can be applied in forensic science for human fingerprint identification, as well as in virus and microorganism research.