There is an increased interest in the need for a noninvasive and nonintrusive biometric identification and recognition system such as Automatic Gait Identification (AGI) due to the rise in crime rates in the US, physical assaults, and global terrorism in public places. AGI, a biometric system based on human gait, can recognize people from a distance and current literature shows that AGI has a 95.75% success rate in a closely controlled laboratory environment. Also, this success rate does not take into consideration the effect of covariate factors such as affective state (mood state); and literature shows that there is a lack of understanding of the effect of affective state on gait biometrics. The purpose of this study was to determine the percent success rate of AGI in an uncontrolled outdoor environment with affective state as the main variable. Affective state was measured using the Profile of Mood State (POMS) scales. Other covariate factors such as footwear or clothes were not considered in this study. The theoretical framework that grounded this study was Murray's theory of total walking cycle. This study included the gait signature of 24 participants from a population of 62 individuals, sampled based on simple random sampling. This quantitative research used empirical methods and a Fourier Series Analysis. Results showed that AGI has a 75% percent success rate in an uncontrolled outdoor environment with affective state. This study contributes to social change by enhancing an understanding of the effect of affective state on gait biometrics for positive identification during and after a crime such as bank robbery when the use of facial identification from a surveillance camera is either not clear or not possible. This may also be used in other countries to detect suicide bombers from a distance.

The aim of this thesis is to provide comprehensive information on the possibilities of authentication, combination of authentication factors and the integration of this issue into corporate communication. The work focuses on this issue and specifies the possibilities for obtaining authentication information, analyses the authentication methods, identification and authorization. It examines the applicability of biometric technologies, the principle of their functionality, examples of their use, their impact, the advantages and disadvantages they bring. A natural, easy-to-use, convenient tool for effective and secure communication is authentication including the dynamic biometric signature. The issues of the dynamic biometric signature technology and its implementation are examined from a comprehensive perspective involving experiments. The research proved that the dynamic biometric signature can serve as a method for supporting secure corporate communication and reduce authentication risks in companies and for individuals.

The main goal of this thesis is to test the feasibility of human identification using the Electrocardiogram (ECG). Such biomedical signal has several key advantages including its intrinsic nature and liveness indicator which makes it more secure compared to some of the existing conventional and traditional biometric modalities. In compliance with the terms and regulations of McMaster University, this work has been assembled into a sandwich thesis format which consist of three journal papers. The main idea of this work is to identify individuals using distance measurement techniques and ECG feature matching. In addition, we gradually developed the content of the three papers. In the first paper, we started with the general criteria for developing ECG based biometric systems. To explain, we proposed both fiducial and non-fiducial approaches to extract the ECG features followed by providing comparative study on the performance of both approaches. Next, we applied non-overlapped data windows to extract the ECG morphological and spectral features. The former set of features include the amplitude and slope differences between the Q, R and S peaks. The later features include extracting magnitudes of the ECG frequency components using short time Fourier Transform (STFT). In addition, we proposed a methodology for QRS detection and segmentation using STFT and binary classification of ECG fiducial features. In the second paper, we proposed a technique for choosing overlapped data windows to extract the abovementioned features. Namely, the dynamic change in the ECG features from heart beats to heartbeat is utilized for identification purposes. To improve the performance of the proposed techniques we developed Frechet-mean based classifier for this application. These classifiers exploit correlation matrix structure that is not accounted for in classical Euclidean techniques. In addition to considering the center of the cluster, the Frechet-mean based techniques account for the shape of the cluster as well. In the third paper, the thesis is extended to address the variability of ECG features over multiple records. Specifically, we developed a multi-level wavelet-based filtering system which utilizes features for multiple ECGs for human identification purposes. In addition, we proposed a soft decision-making technique to combine information collected from multi-level identification channels to reach a common final class. Lastly, we evaluated the robustness of all our proposed methods over several random experiments by changing the testing data and we achieved excellent results. The results of this thesis show that the ECG is a promising biometric modality. We evaluated the performance of the proposed methods on the public ECG ID database because it was originally recorded for biometric purposes. In addition, to make performance evaluation more realistic we used two recordings of the same person obtained under possibly different conditions. Furthermore, we randomly changed both the training and testing data which are obtained from the full ECG records for performance evaluation purposes. However, it is worth mentioning that in all parts of the thesis, various parameters settings are presented to support the main ideas and it is subject to change according to human activity and application requirements. Finally, the thesis concludes with a comparison between all the proposed methods, and it provides suggestions on few open problems that can be considered for future research as extension to the work that has been done in this thesis. Generally, these problems are associated with the constraints on computational time, data volume and ECG clustering. / Thesis / Doctor of Philosophy (PhD)

Indiana University-Purdue University Indianapolis (IUPUI) / The vein structure in the sclera, the white and opaque outer protective covering of the eye, is anecdotally stable over time and unique to each person. As a result, it is well suited for use as a biometric for human identification. A few researchers have performed sclera vein pattern recognition and have reported promising, but low accuracy, initial results. Sclera recognition poses several challenges: the vein structure moves and deforms with the movement of the eye and its surrounding tissues; images of sclera patterns are often defocused and/or saturated; and, most importantly, the vein structure in the sclera is multi-layered and has complex non-linear deformation. The previous approaches in sclera recognition have treated the sclera patterns as a one-layered vein structure, and, as a result, their sclera recognition accuracy is not high. In this thesis, we propose a new method for sclera recognition with the following contributions: First, we developed a color-based sclera region estimation scheme for sclera segmentation. Second, we designed a Gabor wavelet based sclera pattern enhancement method, and an adaptive thresholding method to emphasize and binarize the sclera vein patterns. Third, we proposed a line descriptor based feature extraction, registration, and matching method that is scale-, orientation-, and deformation-invariant, and can mitigate the multi-layered deformation effects and tolerate segmentation error. It is empirically verified using the UBIRIS and IUPUI multi-wavelength databases that the proposed method can perform accurate sclera recognition. In addition, the recognition results are compared to iris recognition algorithms, with very comparable results.

The most common personal authentication techniques used for identity management employ a secret PIN or password that must be remembered. The challenge, for a given user, is that a multitude of such codes must be recalled over the course of the day for transactions involving distinct computer applications. Password mania prevails. Fingerprint biometric technology is an ideal alternate solution to this password recall problem. In spite of their availability for nearly thirty years, fingerprint biometric systems still remain uncommon in public sectors of industry such as education, government, and technology. Technology has improved sufficiently that false acceptance and rejection rates are no longer valid excuses. Two proposed reasons for this lack of deployment are 1) society's misunderstanding regarding the personal privacy, security, and function of the technology; and 2) inadequate education regarding the technology. This present research was structured to test these hypotheses, and attempt to identify the major societal factors that have limited fingerprint biometric eployment in IT authentication systems. Three research approaches regarding acceptance of fingerprint biometric technology by targeted populations were used in this study, namely 1) a personal survey, 2) a personal training exercise, and 3) a web-based survey. Targeted populations included the general public in the State of Utah and its legislative members who made decisions regarding identity management legislation for state departmental functions. Objectives of this research included gaining a better understanding of 1) legislator's perceptions of why past legislation was rejected, and 2) the public's perception of the personal security of the technology. An additional objective was the confirmation that proper education on security issues improves personal confidence in and acceptance of fingerprint biometric technology.

The purpose of this research is to ascertain potential iris scan data variations from near infrared waves derived from fluorescent illumination. Prior studies of iris data variances from infrared wave interference of halogen, incandescent, and sunlight with iris cameras suggest that similar changes may exist under near infrared wavelengths from fluorescent light. The concern is that the fluorescent energy emission may interfere with the near infrared detection of an iris camera. An iris camera is used to measure human eye characteristics known as biometrics. If such infrared emission is statistically significant, then it can alter the validity of the iris scan data. The experiment utilized nine hundred forty-five (945) scans from sixty-three (63) subjects. Measured results showed increased heat from ambient fluorescent illumination does not statistically alter the biometric readings of human eyes. The test results fail to reject that data loss will not occur as heat is increased in the ambient fluorescent light source.

Studies show that smartphone thefts are a significant problem in the United States. [30] With many upcoming proposals to decrease the theft-rate of such devices, investigating new techniques for preventing smartphone theft is an important area of research. The prevalence of new biometric identification techniques for smartphones has led some researchers to propose biometric anti-theft measures for such devices, similar to the current fingerprint authentication system for iOS. Gait identification, a relatively recent field of study, seems to be a good fit for anti-theft because of the non-intrusive nature of passive pattern recognition in walking. In this paper, we reproduce and extend a modern gait recognition technique proposed in Cell Phone-Based Biometrics by testing the technique outside of the laboratory on real users under everyday conditions. We propose how this technique can be applied to create an anti-theft system, and we discuss future developments that will be necessary before such research is ready to be implemented in a release-quality product. Because previous studies have also centered around the ability to differentiate between individual users from a group, we will examine the accuracy of identifying whether or not a specific user is currently using a system. The system proposed in this paper shows results as high as 91% for cross-fold accuracy for some users; however, the predictive accuracy for a single day’s results ranged from 0.8% accuracy to 92.9% accuracy, showing an unreliability that makes such a system unlikely to be useful under the pressure of real-world conditions.

There have been several studies that aim to determine the most superior Weibull parameter recovery approach of specifying a given forest stand’s Weibull diameter distribution, but no consensus has been made. The lack of agreement could be attributed to studies using different moments/percentile prediction models as well as using different plot size data. This study investigates how plot size and prediction model form affects the performance for moments, hybrid, and percentile Weibull parameter recovery approaches. Five plot sizes and three moments/percentile prediction models were used to determine their effects. Weibull parameters were calculated using each recovery method for each plot size and moments/percentile prediction model combination. Each combination’s diameter distribution was recovered and assessed using absolute error index. Results showed that plot size affected rank of precision for parameter recovery methods. Findings suggest that order statistics may be important in recovering Weibull distribution parameters from stand diameter summary statistics.

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