Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, December 2006. / Thesis Advisor(s): Alex Bordetsky. "December 2006." Includes bibliographical references (p. 71-74). Also available in print.

M.Sc. (Information Technology) / Biometrics is a unique method used to identify humans by distinct biological characterises. In recent years biometrics are showing up everywhere from homes, workplaces, schools and banks. This identification method is rapidly replacing existing methods such as passwords since it offers a higher level of security compared to existing methods. Fingerprints are the most common biometric choice. However fingerprint biometrics is showing limitations. Since fingerprints are an external trait, it can be exposed to many situations (cuts, dirt, wear and tear, and skin conditions) that may impact the biometric captured. These factors can cause security and usability issues. There have been a number of successful attempts such as alteration of fingerprints and gummy fingers which are used to bypass fingerprint readers. An emerging biometric called finger-vein recognition was invented to overcome the issues that fingerprint biometrics have. Finger-vein recognition which is based on the vascular patterns that exist inside the finger, claim to have superior usability characteristics where less false acceptance or rejections occur. Since the finger-vein recognition is based on an internal trait it is assumed that external factors such as scars or even dirt will not affect the biometric collected. This dissertation aims to investigate the limitations of fingerprints and to determine whether finger-vein recognition can address these limitations. During the course of the dissertation applicable fields such as construction and mining will be identified for finger-vein recognition where fingerprint recognition has shown weakness. Together, fingerprint and finger-vein technologies will be used in a mining industry to perform minor experiments. The results of these experiments will be used to determine if finger-vein addresses the fundamental limitations of fingerprint biometrics in these industries. The main purposes of the dissertation will be to investigate finger-vein technology, the applicable fields and whether finger-vein recognition can solve the problems fingerprint recognition imposes in certain industries.

No description available.

M.Phil. (Electrical And Electronic Engineering) / The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images.

No description available.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / Biometrics identi es/veri es a person using his/her physiological or behavioral characteristics. It is becoming an important ally for law enforcement and homeland security. Among all the biometric modalities, iris is tested to be the most accurate one. However, most existing methods are not designed for non-cooperative users and cannot work with o -angle or low quality iris images. In this thesis, we propose a robust multi-stage feature extraction and matching approach for non-cooperative iris recognition. We developed the SURF-like method to extract stable feature points, used Gabor Descriptor method for local feature description, and designed the multi- stage feature extraction and matching scheme to improve the recognition accuracy and speed. The related experimental results show that the proposed method is very promising. In addition, two template security enhanced schemes for the proposed non- cooperative iris recognition are introduced. The related experimental results show that these two schemes can e ectively realize cancelability of the enrolled biometric templates while at the same time achieving high accuracy.

Although handwritten signature verification has been extensively researched, it has not achieved optimum accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). Two different feature extraction techniques were used, namely the Enhanced Modified Direction Feature (EMDF) and the Local Directional Pattern feature (LDP). These were used to analyze the effect of using several different distance-based classification techniques. Among the classification techniques used, are the cosine similarity measure, Mahalanobis, Canberra, Manhattan, Euclidean, weighted Euclidean and fractional distances. Additionally, the novel weighted fractional distances, as well as locally optimized resampling of feature vector sizes were tested. The best accuracy was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. This combination of multiple distance-based classification techniques achieved accuracy rate of 89.2% when using the EMDF feature extraction technique, and 90.8% when using the LDP feature extraction technique. These results are comparable to those in literature, where the same feature extraction techniques were classified with SLTs. The best of the distance-based classification techniques were found to produce greater accuracy than the SLTs. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

Naval Postgraduate School's (NPS) Tactical Network Topology (TNT) experiments seek to develop, implement and identify sensor-unmanned vehicle network, and network-centric operations to assist DoD warfighters in the Global War on Terrorism (GWOT). Using biometric data for rapid identification of High Value Targets (HVT) in ground and Maritiime Interdiction Operations (MIO) is critical to the emeging special operations concept. The goal is to explore solutions and operational constraints associated with biometric data analysis and rapid identification by means of adhoc self forming sensor unmanned vehicle (UV) wireless networks. The objectives of this thesis are to look at how biometrics has performed in a testbed environment that is simulating a real special operations environment in theatre. This thesis is meant to explore and explain the biometrics process that was conducted on top of the tactical network and evaluate its performance. This thesis provided the process model for biometrics identification in the tactical networks environment. This thesis also evaluated the length of time that it took to transmit the fingerprint data from the field to the ABIS databvase, with an identification result then sent back to the field. The longest time that was observed was 70 minutes (using low bandwidth Satellite communications), while the shortest time was 4 minutes for reachback to ABIS and 2 minutes for a local database.

Two experiments were conducted to investigate the impact of poser and perceiver gender on the Happiness/Anger Superiority effect and the Female Advantage in facial expression recognition. Happy, neutral, and angry facial expressions were presented on male and female faces under Continuous Flash Suppression (CFS). Participants of both genders indicated when the presented faces broke through the suppression. In the second experiment, angry and happy expressions were reduced to 50% intensity. At full intensity, there was no difference in the reaction time for female neutral and angry faces, but male faces showed a difference in detection between all expressions. Across experiments, male faces were detected later than female faces for all facial expressions. Happiness was generally detected faster than anger, except when on female faces at 50% intensity. No main effect for perceiver gender emerged. It was concluded that happiness is superior to anger in CFS, and that poser gender affects facial expression recognition. / by Sophia Peaco. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.