Machine Learning and Biometrics
Adele Kuzmiakova
9781774691045
244 pages
Arcler Education Inc
Overview
This comprehensive guide provides a detailed overview of modern biometrics, which allows a person to be identified and authenticated based on recognizable, unique, and verifiable data. Biometrics technologies include detection of dormant fingerprints, iris, gait, or facial and voice recognition. Today, biometrics powers cutting-edge security algorithms. Biometrics is used for access into banks, airports or personal smartphones, which means that your money and personal information can be stored safely. Each chapter starts with a comprehensive review of the biometrics technology and its algorithmic description, describes how it works, and outlines all of the applicable and modern use cases of that technology. This book will be an invaluable companion guide to students wishing to become system designers, micro-engineers, security algorithms creators, security experts, and electronic security system manufacturers working on controls or microchips.
Author Bio
Adele Kuzmiakova is a computational engineer focusing on solving problems in machine learning, deep learning, and computer vision. Adele attended Cornell University in New York, United States for her undergraduate studies. She studied engineering with a focus on applied math. While at Cornell, she developed close relationships with professors, which enabled her to get involved in academic research to get hands-on experience with solving computational problems. She was also selected to be Accel Roundtable on Entrepreneurship Education (REE) Fellow at Stanford University and spent 3 months working on entrepreneurship projects to get a taste of entrepreneurship and high-growth ventures in engineering and life sciences. The program culminated in giving a presentation on the startup technology and was judged by Stanford faculty and entrepreneurship experts in Silicon Valley. After graduating from Cornell, Adele worked as a data scientist at Swiss Federal Institute of Technology in Lausanne, Switzerland where she focused on developing algorithms and graphical models to analyze chemical pathways in the atmosphere. Adele also pursued graduate studies at Stanford University in the United States where she entered as a recipient of American Association of University Women International Fellowship. The Fellowship enabled her to focus on tackling important research problems in machine learning and computer vision. Some research problems she worked on at Stanford include detecting air pollution from outdoor public webcam images. Specifically, she modified and set up a variety of pre-trained architectures, such as DehazeNet, VGG, and ResNet, on public webcam images to evaluate their ability to predict air quality based on the degree of haze on pictures. Other deep learning problems Adele worked on include investigating the promise of second-order optimizers in deep learning and using neural networks to predict sequences of data in energy consumption. Adele also places an emphasis on continual education and served as a Student Leader in PyTorch scholarship challenge organized by Udacity. Her roles as the Student Leader were helping students debug their code to train neural networks with PyTorch and providing mentorship on technical and career aspects. Her hobbies include skiing, playing tennis, cooking, and meeting new people.
