Web-Shaped Model for Head Pose Estimation: an Approach for Best Exemplar Selection

Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 well-known face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point '04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

An Analysis and Validation of an Online Photographic Identity Exposure Evaluation System

The rapid growth in volume over the last decade of personal photos placed online due to the advent of social media has made users highly susceptible to malicious forms of attack. A system was proposed and constructed using Open Source technologies capable of acquiring the necessary data to conduct a measurement of online photographic exposure to aid in assessing a user\u27s digital privacy. The system\u27s effectiveness at providing feedback on the level of exposure was tested by using a controlled set of three subjects. Each subject provided three training photos each that simulated what would be easily ascertainable from social media profiles, online professional portfolios, or public photography. The system was able to successfully biometrically identify 23 images out of ~14,000 that related to one of the respective candidates. This validates the system as an automated threat and vetting tool for online photographic privacy. VeriLook 5.4 one-to-many matching grossly underperformed on the images gathered with a mere 21% at best true acceptance rate. The scoring algorithm used herein to evaluate each candidate\u27s online photographic exposure was proven to be effective. The system developed was able to show that a candidate\u27s assumption of their digital footprint size is not always correct. Additional testing of the scoring algorithm is recommended before a conclusion can be made with about its universal accuracy

IMMERSIVE, INTEROPERABLE AND INTUITIVE MIXED REALITY FOR SERVICE IN INDUSTRIAL PLANTS

The authors propose an innovative Mixed Reality solution representing an immersive intuitive and interoperable environment to support service in industrial plants. These methodologies are related to concepts of Industry 4.0. Solutions based on a mix of VR and AR (Virtual and Augmented Reality ) with special attention to the maintenance of industrial machines; indeed the authors propose an overview of this approach and other synergistic techniques. Moreover, alternative instruments are presented and their specific advantages and disadvantages are described. Particularly, the approach is based on the SPIDER, an advanced interoperable interactive CAVE developed by the authors which supports cooperative work of several users involved in training, troubleshooting and supervision are proposed. Last but not least, an overview of projects using same techniques in other fields, such as construction, risk assessment, Virtual Prototyping and Simulation Based Design is presented

Data Mining on the Crawl Frontier: Metaphor in Cybernetic Capitalism

This essay explores how the intangible operations of networked computing-machines are frequently described through tangible metaphors. After looking at their origins in military bureaucracies, this analysis steps through the material operations of Google’s famous search-engine, noting the various metaphors that are used to make sense of it, and the way they frequently draw on colonial and extractive imagery. I give an account of the company’s rise to power, emphasising how their immense profits became possible because of their control of intellectual property rights, as well as over the ‘terms and conditions’. Across this critical analysis, I show how these metaphors embody the dominant worldview of cybernetic capitalism and demonstrate how they serve as ways to cope with the extreme abstractions increasingly enmesh us

Multifactor authentication using smartphone as token

Biometrics are a field of study with relevant developments in the last decade. Specifically, electrocardiogram (ECG) based biometrics are now deemed a reliable source of identification. One of the major advances in this technology was the improvements in off-the-person authentication, by requiring nothing more than dry electrodes or conductive fabrics to acquire an ECG signal in a non-intrusive way through the user’s hands. However, identification still has a relatively poor performance when using large user databases. In this dissertation we suggest using ECG authentication associated with a smartphone security token in order to improve performance and decrease the time required for the recognition. We develop this technique in a user authentication scenario for a Windows login. We developed our solution using both normal Bluetooth (BT) and Bluetooth Low Energy (BLE) technologies to preserve phone battery; also, we develop apps for Windows Phone and Android, due to limitations detected. Additionally, we took advantage of the Intel Edison’s mobility features to create a more versatile environment. Results proved our solution to be possible. We executed a series of tests, through which we observed an improvement in authentication times when compared to a simple ECG identification scenario. Also, ECG performance in terms of false-negatives and false-positives is also increased.A biometria é uma área de estudo que observou desenvolvimentos relevantes na última década. Em específico, a biometria baseada no eletrocardiograma (ECG) é atualmente considerada uma fonte de identificação confiável. Um dos maiores avanços nesta tecnologia consiste na evolução da autenticação off-the-person, que permite realizar a aquisição de sinal de forma não intrusiva usando as mãos do utilizador. Contudo, a identificação através deste método ainda apresenta uma performance relativamente baixa quando usada uma base de dados de dimensão acima das dezenas. Nesta dissertação sugerimos usar a autenticação ECG associada a um telemóvel a funcionar como security token com o objectivo de melhorar a performance e diminuir o tempo necessário para o reconhecimento. Para isso, desenvolvemos a nossa solução usando a tecnologia Bluetooth (BL) clássico, mas também Bluetooth Low Energy (BLE) para preservar a bateria do telemóvel; além disto, desenvolvemos as aplicações em Windows Phone e também Android, dadas as limitações que encontrámos. Para criar um ambiente mais versátil e móvel, usámos a recente plataforma Intel Edison. Os resultados obtidos provam que a nossa solução é viável. Executámos uma série de testes, nos quais observámos uma melhoria nos tempos associados à autenticação quando comparados com o cenário clássico de identificação por ECG. Adicionalmente, a performance do ECG no que diz respeito ao número de falsos-negativos e falsos-positivos apresentou também melhoria

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures