Fig 1
Designation of fingerprint minutiae In this paper the fingerprints in the ATVS-FFp database are matched with the Bozorth algorithm, which consists of three major steps [24] [27]: 1) Transform a minutiae template into an intrafingerprint minutia comparison table (CT) In a template, for each pair of minutiae, a five-variable entry including the length (d) of the line segment between the two minutiae (α1, α2) and the angles (β1, β2) between each minutia and the line connecting the two minutiae, is made into a CT. Two CTs are constructed from a pair of to-bematched fingerprints. One is called the registered CT and the other is called the query CT. 2) Construct inter-fingerprint minutia compatibility tables Compare each entry in the registered fingerprint's CT to the entries in the query fingerprint's CT. If the differences (d) between the lengths of the two segments and those of the corresponding angles (β1, β2) are smaller than or equal to the predefined thresholds, an entry representing the two pairs of minutiae will be made into the inter-fingerprint minutia compatibility table. 3) Traverse the inter-fingerprint compatibility tables to obtain a matching score between the two fingerprints. More details about the algorithm can be found in [28].
Source publication
Fingerprint is a widely used biometrics. Its
extensive usage motivates imposter to fabricate fake
fingerprints. Vitality detection has been proposed to
prevent counterfeit finger attack. Currently the detection
can be done either during the process of acquiring
fingerprint image or by comparing multiple sequentially
acquired images. It is an...
Context in source publication
Context 1
... four subsystems, including acquisition, feature extraction, matching, and decision making. An image produced by the acquisition subsystem is processed and then a template is extracted from the processed image. The template consists of a number of points called minutiae. Most minutiae are points of ridge ending or ridge bifurcation (Refer to Fig. 1). Every minutia is represented with one triple (x, y, θ), where (x, y) is a minutia's Cartesian coordinates, and θ is the ridge orientation at the point. During matching one template will be matched against another template to produce a numerical score. A user-selected threshold is needed to decide whether the two templates match ...
Citations
... In this proposal, Euclidean distance and Hamming distance are used for finding the similarity distances. Gao Q. [7] invented a comparison of fake and real fingerprint images based on different types of sensors. The author used the NIST database and ATVS-FFp database for analysis. ...
A sophisticated mathematical model known as neurosophic extends the vague concept to tackle
real-world issues and applications. The neutrosophic connection of the image can be harnessed to ascertain
its correlation pattern. This paper aims to employ the neutrosophic method for detecting correlations among
fingerprint images using neutrosophic-based pattern analysis. Additionally, it will propose four strategies for
identifying relationships within image data by employing the neutrosophic approach. The study explores the
four fundamental forms of fingerprint images and experiments with various α values. An α value of 0.99 or
higher is favored for image matching.
... In other words, fingerprints have been put to use in a wide variety of contexts. The widespread use of fingerprints in biometrics makes it possible for impostors and fraudsters to forge ©2022 AELS, INDIA fingerprints and frame someone or overcome security systems, as well as forgers to create fake biometric fingerprints [5].When an innocent person's latent fingerprints are planted at a crime scene, this is called forgery [6]. Forgery occurs when a fingerprint already exists on the surface but was deposited by someone else. ...
... Forgery occurs when a fingerprint already exists on the surface but was deposited by someone else. The earliest known instance of a forged fingerprint was discovered in the 1920s [5,6].There are a number of methods for fingerprint forgery that have been described in the literature. These include making a cast of the fingerprint using a mould, transferring the fingerprint from one surface to another using wax paper or saran wrap-like materials, and etching the fingerprints onto metal plates so that the latent prints and the known prints are identical [7,8]. ...
Fingerprints are used for a variety of identity verification applications, including ID cards, access control, clocking in for attendance, at airports for immigration and security, banks for verification, to unlock phones and laptops, and many other applications as the use of biometric systems in daily work increases. Some of these applications include ID cards, access control, clocking in for attendance, etc. One of the forms of biometric identification that is utilised most frequently is the individual's fingerprint. The widespread use of fingerprints provides counterfeiters with a strong incentive to likewise fake biometric fingerprints. Dactyloscopy is significantly impacted by forgeries, despite the fact that it occurs less frequently under these conditions. In addition, the development of technologies such as 3D printing could potentially pose a threat to the safety of fingerprint biometric systems. This study's primary objective was to investigate the process of fingerprint counterfeiting by employing 3D-printed molds in conjunction with gelatin and glycerin casts of dummy fingers.The Automated Fingerprint Identification System was surprisingly able to correctly identify the fingerprints that were taken from these castings (AFIS). In addition to this, the fake finger created out of gelatin and glycerin has the ability to unlock phones and gain access to restricted areas. As a consequence of this, the conclusions of this study highlight the seriousness of the problem and its influence on the biometric security business. In addition, they offer suggestions regarding how law enforcement authorities might address issues of this nature. In order to differentiate between human skin (human finger) and other materials, the authors suggested that sensors may be made more sensitive by monitoring the heartbeat or body temperature (dummy finger).
... Although there are great advances in this technology, these are still areas to be fine-tuned before practical and mainstream use in smartphones, specifically background noise elimination, short speech, intraspeaker variation and channel distortion, as most anti-noise algorithms have limitations in eliminating noise effectively in multiple noise environments [37][38][39]. Biometric passwords like the fingerprint and iris can easily be faked or stolen [49,50]. In 2015, The US Office of Personnel Management lost 5.6 million individual fingerprints during a hacking incident [49]. ...
Pictures are more likely to be remembered than words or text. For smartphone authentication, graphical password interfaces employing both visual objects and auditory cues are more memorable than textual password interfaces among sighted people because the graphical interface evokes visual imagery in the brain. However, interfaces employing visual imagery have not been studied for the blind and visually impaired. The objective of this research is to demonstrate that graphical password interfaces, designed to evoke visual imagery among blind and visually impaired users, improve the ease of use of smartphone authentication systems. We developed and tested two graphical password systems, BlindLoginV2, which employs object picture superiority effect and AudioBlindLogin, which employs auditory cues to enrich the picture superiority effect. We collected quantitative metrics measuring login speed, configuration time and failure rates immediately after training, 1 h later, 1 day later and 1 week later and qualitative evidence through face-to-face interviews. This study shows that blind and visually impaired users benefit from the picture superiority effect and passwords are more memorable, quicker to key in with greater accuracy as compared to 4-character textual password interfaces. Using the authentication system as an example, we demonstrate that visual imagery can be evoked in blind and visually impaired users through careful design of smartphone interfaces and when paired with additional sensory cues such as audio, can significantly improve the ease-of-use and thereby enhance access among visually impaired users to the rich array of security features available in smartphones.
... [5]. Forged latent prints are the prints of innocent people planted at the crime scene or in other words prints exists on the surface but are deposited by person other than whose prints they are [6]. The first fingerprint forgery was recorded to happen in 1920s [5,6]. ...
... Forged latent prints are the prints of innocent people planted at the crime scene or in other words prints exists on the surface but are deposited by person other than whose prints they are [6]. The first fingerprint forgery was recorded to happen in 1920s [5,6]. Fingerprint forgery can be done with various techniques as mentioned in literature such as mold and cast method where a cast of the fingerprint is made from the mold, transfer method where the fingerprints are transferred from one surface to other using wax paper or saran wrap like materials, or with metal plate etching method where the fingerprints are etched on the metal plates and the latent prints and the known prints are the exact replica of each other [7,8]. ...
With the increasing use of a wide Varity biometric systems in day-today work, fingerprints are used for a numerous identity verification application such as ID cards, access control, clock in for attendance, at airport for immigration and security, banks for verification, to unlock phones and laptops and many more. Fingerprints is one of the most widely used biometric system for authentication. The widespread use of fingerprints encourages forgers to create biometric fingerprints as well. Although forgery is less common in these circumstances, it has a significant impact on dactyloscopy.Furthermore, with the advancement of technologies like as 3D printing, the usage of a fingerprint biometric system could represent a security hazard.The primary goal of this research was to investigate fingerprint forgery using dummy fingers manufactured from 3D printed molds and gelatin and glycerin casts.The results were quite shocking that the fingerprints created with these casts were identifiable with the help of the Automated Fingerprint Identification System (AFIS). Also, the dummy finger made with gelatin and glycerin could unlock the phones and the door to restricted area. As a result, the findings of this study emphasize the gravity of the challenge and its impact on the biometric security industry, as well as advice for how law enforcement agencies should deal with such difficulties.One of the suggestions authors gave is that the sensors could be made more sensitive by detecting the heartbeat or body temperature to differentiate between human skin (real finger) and other materials (dummy finger).
... Essentialness discovery has been proposed to forestall fake finger assault [3]. However, fingerprint-based recognition systems are prone to presentation attacks using an artificial replica of a fingerprint image as shown in figure 3. f) Others. ...
... Some excellent results were produced with an accuracy of 98% and average FPR of less than 2%. While studying various sensors, Q. Gao [3] carried out a survey on fake fingerprints and concluded that different sensors produce images with different resolutions which leads to misclassification at times. He further stated that fingerprints generated from different materials also produce different results. ...
The Presentation attacks on biometric systems have become a major concern in the cyber world. The literature reveals that these systems are vulnerable to spoofing or presentation attacks (PAs) which sometimes result in complete failure of the recognition system. To combat against PAs, the anti-spoofing or Presentation attack detection (PAD) methods have been developed to check the liveness of the fingerprint modality. However, with the emergence of artificial intelligence, several software-based countermeasures have been proposed by the research community. Depending on the type feature extraction mechanism the PAD techniques are categorized into two main types namely; Machine learning (ML) and Deep Learning (DL) based approaches. This paper aims to present a review of existing work on the fingerprint PAD mechanism which has been carried out. The paper discusses the various fingerprint artifacts, state-of-the-art ML and DL based PAD algorithms, and publically available benchmark datasets. Furthermore, we discuss the major research challenges and future work that need to be addressed in this active field of fingerprint liveness detection.
... 12 Attacker replays a recorded video clip showing the face of the Facialprint mimicked person captured with the help of a cell phone, video recorder or other handheld device before a facial recognition system. 13 Attacker compels a victim, through brute force, social engineering, Facialprint or any other means to display own facial image before a facial recognition system. 14 Voice ...
... Its density is the Ocular fluid density measured as a ratio of mass per unit volume (kg/m 3 ). Unit of 13 Ocular fluid measurement is ρ which is the Greek small letter Rho. For all density liquids, water is a reference standard fluid with density ρ = 1000kg/m 3 , while for gases air or O2 is a standard fluid with density ρ = 1.293 kg/m 3 . ...
Biometric systems verify humans using their unique physiological or behavioural patterns to offer more secure authentication over passwords and tokens. Despite their benefits, Biometric Authentication Systems remain vulnerable to spoofing, wherein an impostor presents a forged biometric trait and bypasses security checks. Impacts of successful spoofing can be potentially fatal such as in healthcare and crime investigation systems where insecure authentication can result in patient misdiagnosis and criminal misidentification, respectively. Existing anti-spoofing techniques are mostly uni-modal and predictable, and therefore incapable of coping with the sophistication of modern-day biometric cyberattacks. This paper presents the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) framework which employs a complex trait randomization algorithm to mitigate predictability. Fifteen liveness attributes derived from finger, face and iris traits are used to simulate various authentication scenarios, resulting in 99.2% efficiency over uni-modal biometric systems. The paper also proposes areas of useful application of the framework based on its capacity to neutralize an impostor's ability to accurately predict biometric trait combinations at the sensor verification stage.
... Measured as blinks per minute (bpm) totaling up to 4.2 million blinks per year. 10 Lip movement ...
Biometric systems verify humans using their unique physiological or behavioural patterns to offer more secure authentication over passwords and tokens. Despite their benefits, Biometric Authentication Systems remain vulnerable to spoofing, wherein an impostor presents a forged biometric trait and bypasses security checks. Impacts of successful spoofing can be potentially fatal such as in healthcare and crime investigation systems where insecure authentication can result in patient misdiagnosis and criminal misidentification, respectively. Existing anti-spoofing techniques are mostly uni-modal and predictable, and therefore incapable of coping with the sophistication of modern-day biometric cyberattacks. This paper presents the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) framework which employs a complex trait randomization algorithm to mitigate predictability. Fifteen liveness attributes derived from finger, face and iris traits are used to simulate various authentication scenarios, resulting in 99.2% efficiency over uni-modal biometric systems. The paper also proposes areas of useful application of the framework based on its capacity to neutralize an impostor's ability to accurately predict biometric trait combinations at the sensor verification stage.
... 33,41,42 The uniqueness of a fingerprint is due to its overall pattern of friction ridges and its minutiae, such as ending ridges, deltas, bifurcations, dots, and lakes; moreover, a fingerprint does not change and is specific to the individual such that it has used for personal identification. 28,29,41 In addition to its specific structures and shapes, a fingerprint has an electrical property, that is, a resistance of about 2 kΩ to 2 MΩ. 43,44 In order to generate a spoof fingerprint that has characteristics similar to those of a real fingerprint, we printed a latent fingerprint pattern by using an inkjet printer with conductive CNT ink. ...
A spoof fingerprint was fabricated on paper and applied for a spoofing attack to unlock a smartphone on which a capacitive array of sensors had been embedded with a fingerprint recognition algorithm. Using an inkjet printer with an ink made of carbon nanotubes (CNTs), we printed a spoof fingerprint having an electrical and geometric pattern of ridges and furrows comparable to that of the real fingerprint. With this printed spoof fingerprint, we were able to unlock a smartphone successfully; this was due to the good quality of the printed CNT material, which provided electrical conductivities and structural patterns similar to those of the real fingerprint. This result confirms that inkjet-printing CNTs to fabricate a spoof fingerprint on paper is an easy, simple spoofing route from the real fingerprint and suggests a new method for outputting the physical ridges and furrows on a two-dimensional plane.
... The constructed recognition templates are matched with the Bozorth algorithm as described in [52]. ...
The privacy of biometric data needs to be protected. Cancellable biometrics is proposed as an effective mechanism of protecting biometric data. In this paper a novel scheme of constructing cancellable fingerprint minutiae template is proposed. Specifically, each real minutia point from an original template is mapped to a neighbouring fake minutia in a user-specific randomly generated synthetic template using the k-nearest neighbour method. The recognition template is constructed by collecting the neighbouring fake minutiae of the real minutiae. This scheme has two advantages: (1) An attacker needs to capture both the original template and the synthetic template in order to construct the recognition template; (2) A compromised recognition template can be cancelled easily by replacing the synthetic template. Single-neighboured experiments of self-matching, nonself-matching, and imposter matching are carried out on three databases: DB1B from FVC00, DB1B from FVC02, and DB1 from FVC04. Double-neighboured tests are also conducted for DB1B from FVC02. The results show that the constructed recognition templates can perform more accurately than the original templates and it is feasible to construct cancellable fingerprint templates with the proposed approach. Index Terms—Fingerprint, minutiae, pseudo random number generator, synthetic template, k-nearest neighbours, cancellable template.
