A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

Face Recognition using SOM Network

This paper presents novel technique for recognizing faces. From the last two decades, face recognition is playing an important and vital role especially in the field of commercial, banking, social and law enforcement area. It is an interesting application of pattern recognition and hence received significant attention. The complete process of face recognition covers in three stages, face detection, feature extraction and recognition. Various techniques are then needed for these three stages. Also these techniques vary from various other surrounding factors such as face orientation, expression, lighting and background. The Self-Organizing Map (SOM) Neural Network has been used for training of database and simulation of FR system. In this paper the feature extraction methods discrete wavelet transform (DWT), discrete cosine transform (DCT) simulated in MATLAB are explained

AI Art: Machine Visions and Warped Dreams

Can computers be creative? Is algorithmic art just a form of Candy Crush? Cutting through the smoke and mirrors surrounding computation, robotics and artificial intelligence, Joanna Zylinska argues that, to understand the promise of AI for the creative fields, we must not confine ourselves solely to the realm of aesthetics. Instead, we need to address the role and position of the human in the current technical setup – including the associated issues of labour, robotisation and, last but not least, extinction. Offering a critique of the socio-political underpinnings of AI, AI Art: Machine Visions and Warped Dreams raises poignant questions about the conditions of art making and creativity today. The book critically examines artworks that use AI, be it in the form of visual style transfer, algorithmic experiment or critical commentary. It also engages with their predecessors, including robotic art and net art. AI Art includes a project from Zylinska’s own art practice titled ‘View from the Window’, which explores human and nonhuman forms of intelligence, perception and action. The book closes with speculation on future art – and on art’s future

AI Art

Can computers be creative? Is algorithmic art just a form of Candy Crush? Cutting through the smoke and mirrors surrounding computation, robotics and artificial intelligence, Joanna Zylinska argues that, to understand the promise of AI for the creative fields, we must not confine ourselves solely to the realm of aesthetics. Instead, we need to address the role and position of the human in the current technical setup – including the associated issues of labour, robotisation and, last but not least, extinction. Offering a critique of the socio-political underpinnings of AI, AI Art: Machine Visions and Warped Dreams raises poignant questions about the conditions of art making and creativity today. The book critically examines artworks that use AI, be it in the form of visual style transfer, algorithmic experiment or critical commentary. It also engages with their predecessors, including robotic art and net art. AI Art includes a project from Zylinska’s own art practice titled ‘View from the Window’, which explores human and nonhuman forms of intelligence, perception and action. The book closes with speculation on future art – and on art’s future

Patterns of Discrimination: On Photographic Portraits as Documents of Truth in Automated Facial Recognition

Denne avhandlingen tar for seg fotografiers rolle i treningen av ansiktsgjenkjenningsalgoritmer, samt i selve den tekniske prosessen hvor ansikter analyseres. Gjennom en lesning av tre ulike kunstprosjekter som på ulike måter anvender eksisterende ansiktsgjenkjenningsteknologi til å problematisere denne praksisen, etablerer jeg hvordan ulike fordommer – særlig hva angår fotografiets status som objektiv representasjon av verden – påvirker systemenes evne til å analysere ansikter. De aktuelle prosjektene er ImageNet Roulette (2019) av Trevor Paglen og AI-forsker Kate Crawford, How do you see me? (2019) av Heather Dewey-Hagborg, og Spirit is a Bone (2013-15) av kunstner-duoen Broomberg & Chanarin. Problemstillingen som oppgaven forsøker å besvare er som følger: hva kan disse kunstprosjektene fortelle publikum om ansiktsgjenkjenningsteknologi som praksis, og hvilken rolle spiller digitalt fotografi som slike systemers bindeledd til den analoge verden «utenfor» dem selv? Som svar på dette tar avhandlingen for seg selve den tekniske arkitekturen og hvordan den legger føringer for ansiktsgjenkjenningssystemers operasjoner alt i designprosessen. I tillegg diskuteres ansiktsgjenkjenning fra et historisk perspektiv, hvor forsøk på å knytte juridisk identitet til kroppen gjennom fotografi spores helt tilbake til mediets oppfinnelse på 1800-tallet.Kunsthistorie mastergradsoppgaveKUN350MAHF-KU

Exploring Terms and Taxonomies Relating to the Cyber International Relations Research Field: or are "Cyberspace" and "Cyber Space" the same?

This project has at least two facets to it: (1) advancing the algorithms in the sub-field of bibliometrics often referred to as "text mining" whereby hundreds of thousands of documents (such as journal articles) are scanned and relationships amongst words and phrases are established and (2) applying these tools in support of the Explorations in Cyber International Relations (ECIR) research effort. In international relations, it is important that all the parties understand each other. Although dictionaries, glossaries, and other sources tell you what words/phrases are supposed to mean (somewhat complicated by the fact that they often contradict each other), they do not tell you how people are actually using them. As an example, when we started, we assumed that "cyberspace" and "cyber space" were essentially the same word with just a minor variation in punctuation (i.e., the space, or lack thereof, between "cyber" and "space") and that the choice of the punctuation was a rather random occurrence. With that assumption in mind, we would expect that the taxonomies that would be constructed by our algorithms using "cyberspace" and "cyber space" as seed terms would be basically the same. As it turned out, they were quite different, both in overall shape and groupings within the taxonomy. Since the overall field of cyber international relations is so new, understanding the field and how people think about (as evidenced by their actual usage of terminology, and how usage changes over time) is an important goal as part of the overall ECIR project

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Design, Implementation and Evaluation of Hardware Vision Systems Dedicated to Real-Time Face Recognition

Human face recognition is an active area of research spanning several disciplines such as image processing, pattern recognition, and computer vision. Most researches have concentrated on the algorithms of segmentation, feature extraction, and recognition of human faces, which are generally realized by software implementation on standard computers. However, many applications of human face recognition such as human-computer interfaces, model-based video coding, and security control (Kobayashi, 2001, Yeh & Lee, 1999) need to be high-speed and real-time, for example, passing through customs quickly while ensuring security. For the last years, our laboratory has focused on face processing and obtained interesting results concerning face tracking and recognition by implementing original dedicated hardware systems. Our aim is to implement on embedded systems efficient models of unconstrained face tracking and identity verification in arbitrary scenes. The main goal of these various systems is to provide efficient robustness algorithms that only require moderated computation in order 1) to obtain high success rates of face tracking and identity verification and 2) to cope with the drastic real-time constraints. The goal of this chapter is to describe three different hardware platforms dedicated to face recognition. Each of them has been designed, implemented and evaluated in our laboratory