Engineering Commons^™

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

Deep Face Morph Detection Based On Wavelet Decomposition, Poorya Aghdaie

Graduate Theses, Dissertations, and Problem Reports

Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this …

Object Detection And Classification In The Visible And Infrared Spectrums, Domenick D. Poster

Graduate Theses, Dissertations, and Problem Reports

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark …

Ear Biometrics: A Comprehensive Study Of Taxonomy, Detection, And Recognition Methods, Susan Awm El-Naggar

Graduate Theses, Dissertations, and Problem Reports

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the …

An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei

Graduate Theses, Dissertations, and Problem Reports

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to …

Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani

Graduate Theses, Dissertations, and Problem Reports

Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …

Textured Contact Lens Based Iris Presentation Attack In Uncontrolled Environment, Daksha Yadav

Graduate Theses, Dissertations, and Problem Reports

The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. At the same time, iris recognition in the visible spectrum has developed into an active area of research. These scenarios have necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems.

Motivated by these factors, in this thesis, a detailed analysis of the effect of textured contact lenses on iris recognition in …

Short Wave Infrared Imaging System For Night And Day Long Range Facial Recognition And Surveillance, Robert B. Martin

Graduate Theses, Dissertations, and Problem Reports

The capability to detect, observe, and positively identify people at a distance is important to numerous security and defense applications. Traditional solutions for human detection and observation include long-range visible imagers for daytime and thermal infrared imagers for night-time use. Positive identification, through computer face recognition, requires facial imagery that can be repeatably matched to a database of visible spectrum facial mug shots. Nighttime identification at large distances is not possible with visible imagers due to lack of light, or with thermal infrared imagers due to poor correlation with visible facial imagery. An active-SWIR imaging system was developed that is …

Database Anonymization Services, Chad B. Meador

Graduate Theses, Dissertations, and Problem Reports

The progress of technology and the development of powerful databases have made it possible to store and easily access continually increasing amounts of sensitive data about people. Since personal information is becoming common in many different databases, it is vital that this data be hidden to ensure privacy of the individuals whose records are stored in these repositories. Database anonymization is the key to securing these databases by ensuring that database users will be unable to reveal sensitive personal information by intelligently structuring their queries.

We analyzed the structure of the BiomData database which contains images and sound recordings of …