Privacy-preserving biometric person identification

In this paper we investigate multimodal biometric person and object identification. We model this process of multimodal identification as multimodal multiple hypothesis testing with independent modalities. We analyze theoretical performance limits that can be attained in such a multimodal protocol in terms of exponents of average error probability. Furthermore, we address a privacy related issues in this paper. In particular, we consider performance/privacy trade-off due to the indirect multimodal identification performed in a secret subspace and approximate the obtained performance limits using properties of random projections. Finally, a set of experiments exemplifies our findings.

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.

Author's Introduction Our article outlines the need to bring together the literatures on personal and cultural identity – which have thus far been disconnected from one another. As one develops a sense of personal identity (e.g., goals, values, and beliefs), does one also develop a sense of cultural identity (e.g., how the individual and the group are prioritized, as well as attachment to and affiliation with one's ethnic, cultural, and national group)? This question is becoming increasingly important as Western countries become more and more diverse and multicultural, and as the world becomes more and more international and global. Individuals of European descent in the United States, Canada, Western Europe, Australia, and New Zealand, for example, are no longer insulated from other cultural streams. A generation ago, White Americans, for example, held dominion over the United States and over what ‘American culture’ was. With the advent of mass immigration of ethnic and cultural minorities, as well as globalizing technologies such as email, video conferencing, instant messaging, and virtual chatrooms, we are no longer insulated by our borders. Each of us is a citizen of the world, and this is becoming truer and truer over time. Thus, cultural identity and personal identity are increasingly likely to be related – and the ways in which they influence one another needs to be studied. How does my cultural position in my society (and in the world) influence who I am as a person? How do my cultural beliefs and worldviews influence my personal goals, values, and beliefs? Author Recommends Côté, James E., and Levine, Charles G. (2002). Identity formation, agency and culture: A social psychological synthesis . Mahwah, NJ: Lawrence Erlbaum Associates. This book discusses personal identity within the context of culture, and it clearly sets the stage for a discussion of how personal identity is affected by cultural processes. Arnett, Jeffrey Jensen. (2002). The psychology of globalization. American Psychologist , 57 , 774–783. This article discusses the ways in which globalization affects the personal and cultural identities of people around the world. In particular, the article stresses that individuals will be exposed to various cultural streams even if they never leave their countries of origin and do not come into direct contact with people from other parts of the world. The mass exportation of Western television programs, music, dress styles, fashion, and beliefs is leading people to ‘acculturate’ to Western values and behaviors. Bosma, Harke A., and Kunnen, E. Saskia. (2001). Determinants and mechanisms in ego identity development: A review and synthesis. Developmental Review , 21 , 39–66. This article reviews the ways in which personal identity is developed in ‘individualist’ and ‘collectivist’ contexts. In more individualistic contexts, personal identity is developed through active exploration of different alternatives, commitment to one or more of these, and an in‐depth evaluation of the commitment that has been made. In more collectivistic contexts, personal identity is developed through identification with the goals, values, and beliefs of significant others. This article was one of the first to address personal identity more broadly than through a Western lens. Matsumoto, David. (2003). The discrepancy between consensual‐level culture and individual‐level culture. Culture and Psychology , 9 , 89–95. This article argues that individualism and collectivism (and related constructs such as independence and interdependence) operate differently at the between‐individual level than at the between‐culture level. When studying the interface between personal identity and cultural identity, only individual‐level conceptions of cultural identity should be used. National‐level differences in cultural identity constructs tend to be fairly small – suggesting that most of the differences are between individual people. Schwartz, Seth J., Luyckx, Koen, and Vignoles, Vivian L. (editors, forthcoming). Handbook of identity theory and research . New York, NY: Springer. This forthcoming handbook will consist of state‐of‐the‐art summaries and reviews from some of the leading identity scholars in the world. Chapters will focus on many different domains of identity, including personal and cultural identity but also including national, religious/spiritual, sexual, gender, social, and vocational identity. The book will be an important resource both for students and for professors interested in the field of identity. Sample Syllabus Please add all or a portion of a syllabus that might adopt your article and present it in a broader context to the classroom. Eg. Topics for Lecture & Discussion Week 1 – Overview (What is Identity) Cote, James E. 1996. Sociological perspectives on identity formation: The culture‐identity link and identity capital. Journal of Adolescence . 19: 417–428. A framework for understanding identity formation in an interdisciplinary fashion by addressing the relationship between culture and identity. Grotevant, Harold D. 1987. Toward a process model of identity formation. Journal of Adolescent Research . 2: 203–222. Proposes a model for conceptualizing identity formation that is developmental, contextual and life‐span in scope. Four major components are as follows: individual characteristics, contexts of development, identity process in specific domains, and interdependencies among the identity domains. Weeks 2–4 Neo‐Eriksonian Identity Perspectives (Identity Status, Identity Style, Et Cetera) Berzonsky, Michael D. 1989. Identity style: Conceptualization and measurement. Journal of Adolescent Research . 4: 268–282. A conceptualization of three styles of personal problem solving and decision making – and information orientation that actively seeks and evaluates information, a normative orientation that focuses on internalized conventions, and a diffuse orientation that avoids action until affective cues dictate behavioral reactions. This study measures a validity of a self‐report measure of these styles. Meeus, Wim. 1996. Toward a psychosocial analysis of adolescent identity: An evaluation of the epigenetic theory (Erikson) and the identity status model (Marcia). Hurrelmann, Klaus (Ed.); Hamilton, Stephen F (Ed). (1996). Social problems and social contexts in adolescence: Perspectives across boundaries . (pp. 83–104). xiv, 299 pp. Hawthorne, NY: Aldine de Gruyter. This chapter discusses Erikson's identity theory and Marcia's identity status model, with application to adolescent identity. The author evaluates their empirical validity. Weeks 5–7 Social Identity Perspectives Worchel, Stephen; Morales, J. Francisco; Paez, Dario; Deschamps, Jean‐Claude (Eds.). 1998. Social identity: International perspectives. Social identity: International perspectives. xix, 263 pp. Thousand Oaks, CA: Sage Publications, Inc. This books brings together the latest empirical and theoretical findings in the field of

Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.Results. The results can be used for further development of automated access control and management systems using biometric identification.Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Now a day’s security and identification of a person are crucial in real time applications and solving the problems in biometric identification. Objectives: To address the issues in real time, the face and its organ’s identification are the main parts of the human body. The proposed work involves extraction of the face of the real time captured image, after extraction of the face, the organs like eyes, mouth and nose is extracted for identification of exact person. Methods/ Analysis: The present work captures the real time image from cameras or moving video devices for facial recognition using Principal Component Analysis (PCA), the facial features are extracted from both Hidden Markov Model (HMM), Gaussian mixture model (GMM) methods and classified into different organs using Artificial Neural Network (ANN). The features of organs are extracted in different stages, in first stage Eigen values with the help of PCA, second and third stage feature extraction with help GMM and HMM since these three techniques are the most powerful tools for statistical natural image processing. After extraction of organs of the face, the ANN is applied for classifications of eyes, mouth and nose separately. Findings: In the proposed work the facialorgans are separated into threeslight scale images and these are recombined to acquire the appreciationfacial image results like mouth, eyes and nose. The final obtained results shows that the proposed method has been achieved 95.8% recognition accuracy, Fault Rejection Ratio (FRR) is about 93.1% and Fault Acceptance Ratio (FAR) is 1.7 % which are implemented in Matlab2013A.

Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all significant hypotheses. The FDR is especially appropriate for exploratory analyses in which one is interested in finding several significant results among many tests. In this work, we introduce a modified version of the FDR called the “positive false discovery rate” (pFDR). We discuss the advantages and disadvantages of the pFDR and investigate its statistical properties. When assuming the test statistics follow a mixture distribution, we show that the pFDR can be written as a Bayesian posterior probability and can be connected to classification theory. These properties remain asymptotically true under fairly general conditions, even under certain forms of dependence. Also, a new quantity called the “q-value” is introduced and investigated, which is a natural “Bayesian posterior p-value,” or rather the pFDR analogue of the p-value. 1. Introduction. When testing a single hypothesis, one is usually concerned with controlling the false positive rate while maximizing the probability of detecting an effect when one really exists. In statistical terms, we maximize the power conditional on the Type I error rate being at or below some level. The field of multiple hypothesis testing tries to extend this basic paradigm to the situation where several hypotheses are tested simultaneously. One must define an appropriate compound error measure according to the rate of false positives one is willing to encounter. Then a procedure is developed that allows one to control the error rate at a desired level, while maintaining the power of each test as much as possible. The most commonly controlled quantity when testing multiple hypotheses is the family wise error rate (FWER), which is the probability of yielding one or more false positives out of all hypotheses tested. The most familiar example of this is the Bonferroni method. If there are m hypothesis tests, each test is controlled so that the probability of a false positive is less than or equal to α/m for some chosen value of α. It then follows that the overall FWER is less than or equal to α .M any

Duality between multiple testing and selecting

It is a typical feature of high dimensional data analysis, for example a microarray study, that a researcher allows thousands of statistical tests at a time. All inferences for the tests are determined using the p-values; a smaller p-value than the α-level of the test signifies a statistically significant test. As the number of tests increases, the chance of observing some small p-values is very high even when all null hypotheses are true. Consequently, we make wrong conclusions on the hypotheses. This type of potential problem frequently happens when we test several hypotheses simultaneously, i.e., the multiple testing problem. Adjustment of the p-values can redress the problem that arises in multiple hypothesis testing. P-value adjustment methods control error rates [type I error (i.e. false positive) and type II error (i.e. false negative)] for each hypothesis in order to achieve high statistical power while keeping the overall Family Wise Error Rate (FWER) no larger than α, where α is most often set to 0.05. However, researchers also consider the False Discovery Rate (FDR), or Positive False Discovery Rate (pFDR) instead of the type I error in multiple comparison problems for microarray studies. The methods involved in controlling the FDR always provide higher statistical power than the methods involved in controlling the type I error rate while keeping the type II error rate low. In practice, microarray studies involve dependent test statistics (or p-values) because genes can be fully dependent on each other in a complicated biological structure. However, some of the p-value adjustment methods only deal with independent test statistics. Thus, we carry out a simulation study with several methods involved in multiple hypothesis testing.

This work presents a technique to detect whether a signal is almost cyclostationary (ACS) or wide-sense stationary (WSS). Commonly, ACS (and also CS) detectors require a priori knowledge of the cycle period, which in the ACS case is not an integer. To tackle the case of unknown cycle period, we propose an approach that combines a resampling technique, which handles the fractional part of the cycle period and allows the use of the generalized likelihood ratio test (GLRT), with a multiple hypothesis test, which handles the integer part of the cycle period. We control the probability of false alarm based on the known distribution of the individual GLRT statistic, results from order statistics, and the Holm multiple test procedure. To evaluate the performance of the proposed detector we consider a communications example, where simulation results show that the proposed technique outperforms state-of-the-art competitors.

We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.

Multiple hypothesis testing in proteomics: A strategy for experimental work.

The biometric identification of persons became a very important problem nowadays. Various aspects of criminality, and lately terrorism, forced us to increase the financial outlays allocated for safety measures based on biometrics. Fingerprints and face are the most popular biometric features. However, new modalities are still desirable (moreover, nowadays the usage of multimodal systems becomes very popular). An ear seems to be a very interesting one. The auricle has complex and stable structure and is as distinguishable for various persons as the face. During the development of biometric algorithms their proper and reliable testing is one of the most important aspects. For that purpose a test database can be very helpful. There are many test bases with faces, but ear databases are very rare. Moreover, the existing ones are usually strongly limited. Therefore, in this paper a new ear images database (the West Pomeranian University of Technology Ear Database) is presented for usage in various scientific applications, e.g. for testing biometric algorithms.

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers.

Personal and national identities can play a pivotal role in understanding youth well-being in increasingly diverse societies. However, previous research has predominantly focused on Western contexts, overlooking youth from non-Western societies. Furthermore, there is a scarcity of research that simultaneously examined both personal and national identities in relation to well-being. To address this gap, this study focused on youth belonging to a majority group in Japan, confronting augmented cultural variations, and addressed relationships between personal and national identities and their links to well-being. This cross-sectional study included 968 university students in Japan aged 18-29 years (Mage = 20.06, SD = 1.17; 51.34% women) who identified their nationality as Japanese. Participants completed in 2016 self-report questionnaires, including measures of personal and national identity processes and well-being. Personal identity commitment and in-depth exploration were positively associated with national identity exploration and commitment, whereas personal identity reconsideration of commitment was positively associated with national identity exploration. Personal identity commitment and national identity exploration were positively related to well-being, and personal identity reconsideration of commitment was negatively related to well-being. This study contributes to understanding the interplay between personal and national identity processes and their relations with well-being among youth in a majority group outside Western countries. Given the importance of identity for young people's adjustment and for fostering their sense of social responsibility, this study may have significant practical implications for promoting both individual well-being and social cohesion.