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We developed a transparent and flexible, capacitive fingerprint sensor array with multiplexed, simultaneous detection of tactile pressure and finger skin temperature for mobile smart devices. In our approach, networks of hybrid nanostructures using ultra-long metal nanofibers and finer nanowires were formed as transparent, flexible electrodes of a multifunctional sensor array. These sensors exhibited excellent optoelectronic properties and outstanding reliability against mechanical bending. This fingerprint sensor array has a high resolution with good transparency. This sensor offers a capacitance variation ~17 times better than the variation for the same sensor pattern using conventional ITO electrodes. This sensor with the hybrid electrode also operates at high frequencies with negligible degradation in its performance against various noise signals from mobile devices. Furthermore, this fingerprint sensor array can be integrated with all transparent forms of tactile pressure sensors and skin temperature sensors, to enable the detection of a finger pressing on the display. Next-generation mobile security devices require fingerprint sensors that can be incorporated directly into the display. Here, Park et al. demonstrate a highly transparent, multifunctional capacitive fingerprint sensor array that simultaneously detects tactile pressure and finger skin temperature.

Systemic arterial hypertension is a clinical condition of great risk in the development of cardiovascular diseases and it has a high impact on public health. The disease is influenced by modifiable and non-modifiable factors. In that context, Dermatoglyphics is a method of analysis of fingerprints as a mark of biological individuality and that can be related to health, sports, and the prognosis of diseases due to being able to point out the individual with the potential to develop certain diseases. This study aimed to investigate the characteristics of the fingerprints of women with systemic arterial hypertension by comparing them with a control group, which does not present the disease. Thus, we intend to find a dermatoglyphic pattern for Brazilian women with systemic arterial hypertension. The sample in the study consisted of 732 women, 366 with a positive clinical diagnosis for systemic arterial hypertension, and 366 individuals forming a control group, which did not present systemic arterial hypertension. All individuals in the sample are of equivalent age and the fingerprints were collected from all fingers. The method used to determine the profile of the individuals is the computerized dermatoglyphic. It was used, for the collection of fingerprints, of the Dermatoglyphic Reader®, which presents results of 400% more precision. There was a statistically significant difference between the groups, and when the Adjusted Residue Analysis was performed, the Ulnar Loop figure on fingers 4 and 5 of the left hand, and fingers 1 and 5 of the right hand, was predominant in the group of women with hypertension. These results demonstrate the existence of a dermatoglyphic mark, characteristic of patients with systemic arterial hypertension. Therefore, it can be concluded that the analysis of fingerprints of the hands by the Dermatoglyphic method can demonstrate the potential that women could have developing systemic arterial hypertension.

Background: To assess the relationship between dermatoglyphic patterns and various growth patterns of the mandible. Methods: Patients with Class I Skeletal relation were selected after clinical diagnosis followed by digitally tracing the cephalogram. The patients were subdivided into three groups of mandibular divergence patterns ie Average, Horizontal and Vertical. 90 samples ie 30 in each group were selected for the study. The fingerprints of all the selected subjects were then extracted digitally and analysed for the most dominant pattern in each hand. Results: For the left hand, there was a statistically significant (P<0.05) association between fingerprint pattern and growth pattern when Horizontal growers were compared to Average and Vertical Growers. For the right hand, there was a statistically significant (P<0.05) association between fingerprint pattern and growth pattern when Horizontal growers were compared to Average Growers. A significant association (P<0.05) between fingerprint pattern and growth pattern was also found when average growers were compared to vertical growers. Conclusions: Horizontal growers had 80% frequency of appearance of whorls in their left hand and 67% in their right hand. Horizontal growers could easily be differentiated from the average and vertical growers because of the dominance of whorl pattern in their hands. Composite and arch pattern were more frequent in vertical growers when compared to horizontal and average growers.

Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject’s age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that ( i ) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and ( ii ) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.

Background: Dermatoglyphics is considered, in the scientific milieu, to be an epigenetic marker. The objective of this study was to analyze the presence of dermatoglyphic marks characteristic of neuromotor capacity and abdominal resistance in children and adolescents. Methods: This is a cross-sectional study. The sample consisted of 1,002 individuals, female children and adolescents between the ages of 10 and 16, from public and private schools in the city of Joaçaba, Santa Catarina, Brazil. The protocol selected for analyzing the fingerprints was dermatoglyphics, proposed by Cummins and Midlo using a Dermatoglyphic Reader. The Brazilian Sports Project Manual - PROESP 2015 was used to collect data on muscle strength motor tests. Results: The results showed the presence of a dermatoglyphic mark characteristic of abdominal motor capacity and muscle strength in females. A higher frequency of arches was identified in MET4 and whorls in MET5 and MDT4 in the Risk Zone group. In the Healthy Zone group, ulnar loop was found to be more frequent in MET4, MET5, and MDT4 fingers. Conclusions: The results demonstrated a predictive marker for abdominal motor capacity and strength in females through dermatoglyphics.

The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. The National Research Council of the National Academies and the legal and forensic sciences communities have called for research to measure the accuracy and reliability of latent print examiners' decisions, a challenging and complex problem in need of systematic analysis. Our research is focused on the development of empirical approaches to studying this problem. Here, we report on the first large-scale study of the accuracy and reliability of latent print examiners' decisions, in which 169 latent print examiners each compared approximately 100 pairs of latent and exemplar fingerprints from a pool of 744 pairs. The fingerprints were selected to include a range of attributes and quality encountered in forensic casework, and to be comparable to searches of an automated fingerprint identification system containing more than 58 million subjects. This study evaluated examiners on key decision points in the fingerprint examination process; procedures used operationally include additional safeguards designed to minimize errors. Five examiners made false positive errors for an overall false positive rate of 0.1%. Eighty-five percent of examiners made at least one false negative error for an overall false negative rate of 7.5%. Independent examination of the same comparisons by different participants (analogous to blind verification) was found to detect all false positive errors and the majority of false negative errors in this study. Examiners frequently differed on whether fingerprints were suitable for reaching a conclusion.

Biometrics-based authentication mechanisms can address the built-in weakness of conventional password or token-based authentication in identifying genuine users. However, 2D-based fingerprint biometrics authentication faces the problem of sensor spoofing attacks. In addition, most 2D fingerprint sensors are contact-based, which can boost the spread of deadly diseases such as the COVID-19 virus. Three-dimensional fingerprint-based recognition is the emerging technology that can effectively address the above issues. A 3D fingerprint is captured contactlessly and can be represented by a 3D point cloud, which is strong against sensor spoofing attacks. To apply conventional 2D fingerprint recognition methods to 3D fingerprints, the 3D point cloud needs to be converted into a 2D gray-scale image. However, the contrast of the generated image is often not of good quality for direct matching. In this work, we propose an image segmentation approach using the deep learning U-Net to enhance the fingerprint contrast. The enhanced fingerprint images are then used for conventional fingerprint recognition. By applying the proposed method, the fingerprint recognition Equal Error Rate (EER) in experiment A and B improved from 41.32% and 41.97% to 13.96 and 12.49%, respectively, over the public dataset.

Dermatoglyphics can be used as a supporting tool in the early detection of type 2 Diabetes Mellitus in women. The present study aims to investigate the fingerprints of women with type 2 diabetes mellitus through the dermatoglyphic method, and to compare them with women without the disease. It was conducted by obtaining the fingerprints of all 10 fingers of 268 women – which is known as the dermatoglyphic method –, using the Dermatoglyphic Reader®, with data processed in SPSS (IBM SPSS), version 20.0, and a significance level of p< 0.05. The researched groups are homogeneous for the age, weight and height variables. The group of women with diabetes had a higher average number of lines on the left thumb, as well as the highest total number of lines on the left hand. Moreover, they had a greater number of deltas, in addition to presenting the whorl shape on fingers 1 to 5 of the left hand, and 1 to 4 of the right hand. We concluded that women with type 2 diabetes had a mark of observation concerning their biological individuality on their fingerprints that differs from that of women without the disease.

Background and Hypothesis The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprints geometrical patterns may require flexible algorithms capable of characterizing such complexity. Study Design Based on an initial sample of scanned fingerprints from 612 patients with a diagnosis of non-affective psychosis and 844 healthy subjects, we have built deep learning classification algorithms based on convolutional neural networks. Previously, the general architecture of the network was chosen from exploratory fittings carried out with an independent fingerprint dataset from the National Institute of Standards and Technology. The network architecture was then applied for building classification algorithms (patients vs controls) based on single fingers and multi-input models. Unbiased estimates of classification accuracy were obtained by applying a 5-fold cross-validation scheme. Study Results The highest level of accuracy from networks based on single fingers was achieved by the right thumb network (weighted validation accuracy = 68%), while the highest accuracy from the multi-input models was attained by the model that simultaneously used images from the left thumb, index and middle fingers (weighted validation accuracy = 70%). Conclusion Although fitted models were based on data from patients with a well established diagnosis, since fingerprints remain lifelong stable after birth, our results imply that fingerprints may be applied as early predictors of psychosis. Specially, if they are used in high prevalence subpopulations such as those of individuals at high risk for psychosis.