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Fingerprint skin moisture impact on biometric performance
Abstract
Dry or wet fingers have an impact on biometric performance and the measurement of skin characteristics is important for actionable quality. We collected a ground truth fingerprint skin moisture dataset containing 6600 samples from 33 subjects using 5 optical sensors, two claimed to work well in extreme skin moisture conditions. Controlling conditions by applying cream, alcohol and water we performed objective measurements of skin moisture prior to each fingerprint capture. Analyzing the relationship between measured moisture and biometric performance across five sensors and comparison scores from 3 commercial providers confirms claims to resistance to excessive moisture. Results indicate that sensors resistant to excessive moisture perform worse on dry fingerprints than sensors not resistant to excessive moisture. The collected dataset is made available as an in-house dataset.
... The sensitivity of fingerprint sensors to variability in skin hydration is well known. It can degrade the quality of acquired fingerprints and hence also recognition reliability [105]. Severe dryness can even prevent successful acquisition as illustrated in Fig. 6, thereby resulting in failures to acquire. ...
... Example of a dry fingerprint and the same fingerprint with normal moist (taken from[105]).(a) Stationary touchless6 (b) Mobile touchless ...
Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voice-and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint-and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.
... However, in some cases, these sensors may not work as satisfactorily as conventional fingerprint sensors. The optical underdisplay fingerprint sensor, which recognizes a fingerprint using the difference in light reflected at the finger ridge and valley areas, has an issue in recognizing dry fingers, which cannot make uniform and consistent contact with the surface of the sensor cover layer 12,13 . This issue arises because the sensor may identify a finger ridge area, which is not making good surface contact, as a valley area since part of the light will be reflected at the cover surface instead of propagating through the finger on the surface. ...
... Table 1 shows the calculation results for several different cases where the sensor patterns are composed of different numbers of arrays, while the fingerprint sensing pixel pitches are in the reasonable range of 70-85 μm, which correspond to resolutions of 300-363 dpi. Fingerprint images in this resolution range can be effectively used in commercial devices employing a pattern-based matching algorithm 12,21 . The difference between C M-R and C M-V , denoted ΔC M , is shown in the table instead of the capacitance values because the amplitude of the sensor output signal is determined by ΔC M . ...
In this study, a mutual capacitive-type on-screen fingerprint sensor, which can recognize fingerprints on a display screen to provide smartphones with full-screen displays with a minimal bezel area, is fabricated. On-screen fingerprint sensors are fabricated using an indium tin oxide transparent conductor with a sheet resistance of ~10 Ω/sq. and a transmittance of ~94% (~86% with the substrate effect) in the visible wavelength range, and assembled onto a display panel. Even at this high transmittance, the electrodes can degrade the display quality when they are placed on the display. The interference between periodic display pixel arrays and sensor patterns can lead to the Moiré phenomenon. It is necessary to find an appropriate sensor pattern that minimizes the Moiré pattern, while maintaining the signal sensitivity. To search for appropriate patterns, a numerical calculation is carried out over wide ranges of pitches and rotation angles. The range is narrowed for an experimental evaluation, which is used to finally determine the sensor design. As the selected sensor pitches are too small to detect capacitance variations, three unit patterns are electrically connected to obtain a unit block generating a larger signal. By applying the selected sensor pattern and circuit driving by block, fingerprint sensing on a display is demonstrated with a prototype built on a commercial smartphone.
... The optical under-display fingerprint sensor exploits the difference in light reflected from finger ridges and valley areas to recognize fingerprints, but it has difficulty distinguishing dry fingers, which do not create regular and consistent contact with the sensor cover layer [86,164]. In terms of identification time, recognition of dry fingers, and manufacturing yields, ultrasonic under-display fingerprint sensors have the potential for development. ...
Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).
... With commercial fingerprint scanners, capturing fingerprints in many non-standard, but nonetheless relevant situations can be problematic. For example, excessively wet or dry skin and skin damage (by injury or disease) can severely impair the quality of fingerprint samples [3][4][5]. In infants, the skin is still very soft, which causes their fingerprint patterns to be squashed when pressed against a sensor surface. ...
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis ("outer fingerprint"), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis ("inner fingerprint"). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
... As grease can overpower ridge characteristic, the result, as shown in Fig. 2(c), shows it diminished the sensing area and characteristics of the valleys. Moreover, this core absence reduces not only the detectability of ridge frequency, but also the functionality of other security detection techniques, e.g., core point profile or location detection [18], [19], [20]. ...
The sensitivity of existing fingerprint sensors (FPSs) can decrease considerably owing to environmental factors and parasitic capacitance. In order to overcome this limitation, this paper proposes a highly-sensitive 300 dpi mutual-capacitive transparent fingerprint sensor (FPS) with uniquely designed reference lines for device security. Specifically, the reference lines of the FPS induce capacitance cancellation. Images of fingertips under dry, wet, and oily surface conditions were obtained in the presence and absence of the reference lines. The results showed that the fingerprints were significantly distorted in anomalous surface environments when the reference lines were not used. However, when the reference lines were used, the sensitivity improved irrespective of the environmental conditions. With the edge-detection processing, the proposed FPS exhibited 9.25 %, 61.49 %, and 8.60 % increase in the ridge sensing improvement (RSI) of dry, oil, and wet condition, respectively, thus significantly enhancing the sensing capability. Therefore, we believe the proposed FPS can increase device security owing to its excellent performance.
... Lotion is used to keep the skin soft, hydrated, and moisturized (Merkel et al. 2011;Arndt 1998). Hand sanitizer is used to kill the infectious agents (fungi, bacteria, and virus) present on the hands (Chadwick et al. 2017;Olsen et al. 2015). In India, mehendi is especially used by women to color the epidermal layer of the palmer and planter surfaces during some rituals and ceremonies. ...
Background
In this modern era, advancement in technology is seen in every aspect of our life making it comparatively much easier. Likewise, in the field of fingerprinting, the digital scanners have replaced conventional methods of taking fingerprints, as it is accurate and less time-consuming. In daily life, people often apply oils, lotions, hand sanitizers, and occasionally mehendi on their hands. These cosmetic and daily use products affect the digital recording of fingerprints, thus making it difficult for forensic experts to identify the real offender in many cases. The purpose of the study was to check the effect of oils, lotions, hand sanitizers, and mehendi on the fingerprint pattern.
Results
The present study was undertaken by taking 2700 fingerprints from 30 individuals. These fingerprints were recorded with the help of the SecuGen Hamster IV fingerprint scanner under controlled environmental conditions. The examination and comparison of fingerprint patterns were done on the basis of visibility (clarity and intensity). The presence of cosmetic and daily use products affected the visibility of digitally captured fingerprints. Different products caused different effects based on their properties. Synthetic mehendi, alcohol-based hand sanitizer, greasy lotion, and viscous oil caused significant differences in the fingerprint images by degrading the fingerprint quality. The non-greasy lotion and non-alcohol-based hand sanitizer showed less effect, whereas non-viscous oil and natural mehendi caused a minimal effect on the quality of fingerprint images.
Conclusion
The application of cosmetic and daily use products added an additional layer on the fingers which is not present naturally. The additional layer caused alterations in the fingerprint pattern of an individual. So, digital fingerprints should be collected after proper washing of hands.
... Although the amount of moisture on users' fingers has an impact on overall biometric performance [114], the effects of humidity on the survival of microorganisms on environmental surfaces such as fingerprint scanners vary greatly between different classes of microorganisms and are significantly influenced by the presence of a cell wall or protective membrane. ...
Biometric systems use scanners to verify the identity of human beings by measuring the patterns of their behavioral or physiological
characteristics. Some biometric systems are contactless and do not require direct touch to perform these measurements; others,
such as fingerprint verification systems, require the user to make direct physical contact with the scanner for a specified duration
for the biometric pattern of the user to be properly read and measured. This may increase the possibility of contamination with
harmful microbial pathogens or of cross-contamination of food and water by subsequent users. Physical contact also increases
the likelihood of inoculation of harmful microbial pathogens into the respiratory tract, thereby triggering infectious diseases. In
this viewpoint, we establish the likelihood of infectious disease transmission through touch-based fingerprint biometric devices
and discuss control measures to curb the spread of infectious diseases, including COVID-19.
... Although the amount of moisture on users' fingers has an impact on overall biometric performance [114], the effects of humidity on the survival of microorganisms on environmental surfaces such as fingerprint scanners vary greatly between different classes of microorganisms and are significantly influenced by the presence of a cell wall or protective membrane. ...
UNSTRUCTURED
Biometric systems verify humans using scanners to measure the patterns of their behavioural or physiological characteristics. While contactless biometric scanners like facial recognition obtain facial pattern measurements without direct touch, contact-based biometric scanners such as fingerprint verification systems require physical contact as part of the process of obtaining the biometric sample needed for recognition. The process requires the user to directly place the thumb on the scanner in a certain manner for a specified duration in order for the biometric pattern to be properly read and measured. The direct placement of the thumb on the scanner surface could potentially increase the chances of contamination with harmful microorganisms including pathogens, fungi, bacteria, and viruses, cross contamination of hands of subsequent users with unintentional transfer to food and water, as well as direct inoculation into the respiratory tract thereby triggering infectious diseases. This paper, a viewpoint on the transmissibility of infectious diseases through touch-based fingerprint biometric device, proposes hygienic measures to curbing the spread of infectious diseases including the novel coronavirus disease (COVID-19).
... For the performance evaluation, we have chosen to assess the predictive performance of NFIQ and the quality features specified in [41] databases which are summarised in Section 4. For the dataset WDSET02 5 different capture devices were used and the capture protocol specified four finger surface conditions: no treatment with capture as the subject arrives; application of an alcohol based solution to dry the skin surface; application of hand-moisturiser; and finally soaking in water. FVC2004 DB1 and FVC2006 DB2A were captured in a supervised manner were subjects were instructed to perform various actions which result in various degradations such as elastic deformation and uneven pressure. ...
Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. The authors' evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.
A Personal Identification Number (PIN) is a widely adopted authentication method used by smartphones, ATMs, etc. PINs offer strong security and can be reset when compromised (unlike biometric authentication). However, PINs can be inaccessible for blind or low vision (BLV) users due to screen readers voicing PINs to bystanders or potential shoulder surfing attack risks-bystanders could watch the PIN being entered without the user noticing. To address this, we present OneButtonPIN, an interface to improve PIN entry accessibility and security for BLV users. Here, a single on-screen button, when pressed and held, triggers a haptic vibration sequence. A digit is entered by counting the vibrations and releasing the button. We explored introducing random timings to the vibration sequence to increase security. A week-long evaluation with 9 BLV participants and a security study with 10 sighted participants acting as shoulder surfers demonstrated OneButtonPIN's usability and resilience against eavesdropping.
PIN (Personal Identification Number) code entry is a widely used authentication method used on smartphones, ATMs, etc. However, it is typically subject to shoulder surfing attacks where, a bystander may observe the user’s keypad during PIN code entry. To mitigate this issue, we present a novel method that uses non-visual keypads for entering PIN codes where, the numbers on the keypad are invisible and rearranged. The numbers are rearranged by shifting the rows/columns of the keypad and we use haptic patterns to privately convey the keypad layout information to the user. The results of our first study with 22 participants indicated that the participants could learn the haptic patterns relatively fast and use the non-visual keypads with high accuracy. Next, a security experiment with 12 participants discusses the effectiveness of our method in mitigating shoulder surfing attacks.KeywordsHapticAuthenticationPIN codesUsable securityMobile
The recognition performance of Automatic Fingerprint Identification System (AFIS) is immensely affected by the quality of the input fingerprint images. In a low-quality fingerprint image, various spurious minutiae points may be detected which may degrade the recognition performance of the AFIS system. Effective analysis of the low-quality fingerprint images prior to the fingerprint matching stage can aid in improving the recognition performance of the system. In this work, low quality fingerprint images are identified using a well known local textural descriptors called local phase quantization (LPQ). The local texture descriptors are gaining popularity due to their excellent performance and flexibility in analyzing the texture patterns. The experimental evaluations are carried out on low quality fingerprint images of publicly available FVC 2004 DB1 dataset. The achieved results show the high performance and robustness of the proposed method. As the proposed method outperforms the current state-of-the-art fingerprint classification methods, it can be utilized as a quality control unit during the fingerprint acquisition phase of the AFIS. The proposed method also has an advantage of computing only a single feature for fingerprint quality classification which makes it simple and fast approach.
This paper reports the correlations between skin characteristics, such as moisture, oiliness, elasticity, and temperature of the skin, and fingerprint image quality across three sensing technologies. Fingerprint images from the index finger of the dominant hand of 190 individuals, were collected on nine different fingerprint sensors. The sensors included four capacitance sensors, four optical sensors and one thermal fingerprint sensor. Skin characteristics included temperature, moisture, oiliness and elasticity, were measured prior to the initial interaction with each of the individual sensors. The analysis of the full dataset indicated that the sensing technology and interaction type (swipe or touch) were moderately and weakly correlated respectively with image quality scores. Correlation analysis between image quality scores and the skin characteristics were also made on subsets of data, divided by the sensing technology. The results did not identify any significant correlations. This indicates that further work is necessary to determine the type of relationship between the variables, and how they impact image quality and matching performance.
Measurement of stratum corneum (SC) hydration often involves the use of commercial instruments. The aim of this study was to compare and validate two recent instruments: the Corneometer 825® (digital probe) and the Skicon-200 EX®.
In vitro calibration was carried out on filter pads using different solvents, measurements over different layers of plastic foils, and evaluation of desorption kinetics. In vivo measurements were carried out on skin sites covering a range of very dry to well-hydrated skin areas.
Conductance measurements are influenced by electrolytes while capacitance measurements are not. Dielectric constant of the solvents influences the values of both instruments (r respectively 0.92 and 0.99). The capacitance method carries information from deeper layers (up to 45 μm) compared with the conductance instrument (up to 15 μm). Desorption experiments show a strong relation between the amount of water and respectively the capacitance and the conductance values. The in vivo experiments revealed a strong relation between the two methods (r = 0.97). Sensitivity of the capacitance method is limited for the highest hydration values.
Both instruments allow a certain calibration and both give good estimates of the SC hydration.
This study evaluated fingerprint quality across two populations, elderly and young, in order to assess age and moisture as potential factors affecting utility image quality. Specifically, the examination of these variables was conducted on a population over the age of 62, and a population between the ages of 18 and 25, using two fingerprint recognition devices (capacitance and optical). Collected individual variables included: age, gender, ethnic background, handedness, moisture content of each index finger, occupation(s), subject's use of hand moisturizer, and prior usage of fingerprint devices. Computed performance measures included failure to enroll, and quality scores. The results indicated there was statistically significant evidence that both age and moisture affected effectiveness image quality of each index finger at α=0.01 on the optical device, and there was statistically significant evidence that age affected effectiveness image quality of each index finger on the capacitance device, but moisture was only significant for the right index finger at α=0.01.
Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. The authors' evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.
This report discusses the flat-to-flat matching performance of the US-VISIT fingerprint matching system. Both one-to-many matching used to detect duplicate visa enrollments and one-to-one matching used to verify the identity of the visa holder are discussed. With the proper selection of an operating point, the one-to-many accuracy for a two-finger comparison against a database of 6,000,000 subjects is 95% with a false match rate of 0.08%. Using two fingers, the one-to-one matching accuracy is 99.5% with a false accept rate of 0.1%.
In an automatic fingerprint identification system, it is desirable to estimate the image quality of the fingerprint image
before it is processed for feature extraction. This helps in deciding on the type of image enhancements that are needed and
in deciding on thresholds for the matcher in the case that dynamic thresholds are used. In this paper, we propose a Gabor-feature
based method for determining the quality of the fingerprint images. An image is divided into Nw x w blocks. Gabor features of each block are computed first, then the standard deviation of the M Gabor features is used to determine the quality of this block. The results are compared with an existing model of quality
estimation. Our analysis shows that our method can estimate the image quality accurately.
Background/purpose:
MoistureMeter is a novel capacitive device for measuring the hydration of stratum corneum (SC). The capacitor 'plates' are formed by the probe material and the well-conducting epidermal-dermal skin layers, while the dry layer of SC acts as an 'insulator' of the capacitor. Due to this measurement principle, the measurement depth is not constant, but equal to the thickness of the dry layer of SC. The present study was undertaken to test the MoistureMeter SC-2 in an experimental setup simulating the uppermost skin structure. The sensitivity of the MoistureMeter was compared with a conventional device Corneometer CM 820 in eight healthy volunteers.
Methods:
The experimental setup consisted of a polyethene foil simulating SC and a mixture of cellulose and saline simulating the underlying skin layers with a high water content. The hydration of SC of volar forearm was measured in eight healthy volunteers both with the MoistureMeter SC-2 and Corneometer CM 820 after the application of three different skin formulations: base, base plus salt (2 wt% NaCl), base plus salt and glycerin (5 wt%). In the sorption-desorption test, the skin was wetted with a drop of water and the dehydration was followed with both devices for 2 min.
Results:
The test with the experimental setup agreed with the results predicted by the mathematical capacitance model. The hydration values of the MoistureMeter SC-2 and Corneometer CM 820 correlated well (R=0.75), but the relative range of readings with the MoistureMeter was approximately three times larger than with Corneometer. The MoistureMeter was insensitive for the salt (2 wt% NaCl) of the formulation and differentiated the hydration effect of glycerin better than the Corneometer. In the sorption-desorption test, the MoistureMeter demonstrated the individual differences between the volunteers, whereas with the Corneometer the differences were minimal.
Conclusion:
The capacitive measuring principle of the MoistureMeter was demonstrated in an experimental arrangement. Although both instruments yielded equivalent data with the base formulation, the MoistureMeter was a more sensitive device than the Corneometer CM 820 and independent of added salt in the formulations.
We document methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality. We are motivated by a need to test claims that quality measures are predictive of matching performance. We regard a quality measurement algorithm as a black box that converts an input sample to an output scalar. We evaluate it by quantifying the association between those values and observed matching results. We advance detection error trade-off and error versus reject characteristics as metrics for the comparative evaluation of sample quality measurement algorithms. We proceed this with a definition of sample quality, a description of the operational use of quality measures. We emphasize the performance goal by including a procedure for annotating the samples of a reference corpus with quality values derived from empirical recognition scores.
We present a novel measure of fingerprint image quality, which can be used to estimate fingerprint match performance. This means presenting the matcher with good quality fingerprint images will result in high matcher performance, and vice versa, the matcher will perform poorly for poor quality fingerprints. We discuss the implementation of our fingerprint image quality metric and we present the results of testing it on 280 different combinations of fingerprint image data and fingerprint matcher systems. We found that the metric predicts matcher performance for all systems and datasets. Our definition of quality can be applied to other biometric modalities and upon proper feature extraction can be used to assess quality of any mode of biometric samples.
Discusses methods to estimate the quality as well as validity of a fingerprint image. Orientation certainty is used to certify the localized texture pattern of the fingerprint images while ridge to valley structure is analyzed to detect invalid images. Global uniformity and continuity ensures that the image is valid as a whole. 150 images with various qualities are evaluated using the proposed algorithm and quality benchmark we defined. A monotonic relationship is found indicating that the proposed algorithm is feasible in detecting low quality as well as invalid fingerprint images.
One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19 200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.
Unique Identification Authority of India Available: print
- Aadhaar
Vladimir Smida, and Christoph Busch, Finger image quality assessment features – definitions and evaluation
- A Martin
- Olsen