Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings

In the realm of security applications, biometric authentication systems play a crucial role, yet one often encounters challenges concerning privacy and security while developing one. One of the most fundamental challenges lies in avoiding storing biometrics directly in the storage but still achieving decently high accuracy. Addressing this issue, we contribute to both artificial intelligence and engineering fields. We introduce an innovative image distortion technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. From the theoretical perspective, we explore how reliable state-of-the-art biometrics recognition neural networks are by checking the maximal degree of image distortion, which leaves the predicted identity unchanged. On the other hand, applying this technique demonstrates a practical solution to the engineering challenge of balancing security, precision, and performance in biometric authentication systems. Through experimenting on the widely used datasets, we assess the effectiveness of our method in preserving AI feature representation and distorting relative to conventional metrics. We also compare our method with previously used approaches

GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS

Abstract. Point clouds obtained via Terrestrial Laser Scanning (TLS) surveys of historical buildings are generally transformed into semantically structured 3D models with manual and time-consuming workflows. The importance of automatizing this process is widely recognized within the research community. Recently, deep neural architectures have been applied for semantic segmentation of point clouds, but few studies have evaluated them in the Cultural Heritage domain, where complex shapes and mouldings make this task challenging. In this paper, we describe our experiments with the DGCNN architecture to semantically segment historical buildings point clouds, acquired with TLS. We propose a variation of the original approach where a radius distance based technique is used instead of K-Nearest Neighbors (KNN) to represent the neighborhood of points. We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona

AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection

Biometric security is the cornerstone of modern identity verification and authentication systems, where the integrity and reliability of biometric samples is of paramount importance. This paper introduces AttackNet, a bespoke Convolutional Neural Network architecture, meticulously designed to combat spoofing threats in biometric systems. Rooted in deep learning methodologies, this model offers a layered defense mechanism, seamlessly transitioning from low-level feature extraction to high-level pattern discernment. Three distinctive architectural phases form the crux of the model, each underpinned by judiciously chosen activation functions, normalization techniques, and dropout layers to ensure robustness and resilience against adversarial attacks. Benchmarking our model across diverse datasets affirms its prowess, showcasing superior performance metrics in comparison to contemporary models. Furthermore, a detailed comparative analysis accentuates the model's efficacy, drawing parallels with prevailing state-of-the-art methodologies. Through iterative refinement and an informed architectural strategy, AttackNet underscores the potential of deep learning in safeguarding the future of biometric security

Energy harvesting applied to smart shoes

The appeal of energy harvesting systems lies in the possibility of capturing free energy that would be dissipated and is therefore obtainable without costs. Today, advanced techniques and devices exist for capturing from the environment, storing, and managing quotas of natural energy, which are made available in the form of electrical energy. At the same time, the most recent microprocessors grant an extremely high power efficiency, which permits their operation with minimal power consumption. As a consequence, low-consuming devices can be power supplied by using energy harvesting systems. If this concept is applied to wearable electronics, the most efficient choice is that of exploiting the energy released when the users walk, by developing systems that are embedded in the shoe sole. At each step, the force exerted on the device can be transformed into a relatively high amount of electrical energy, for example by using piezoelectric elements and electromagnetic induction systems. The paper describes the design of four different solutions for smart shoes that make use of energy harvesting apparatuses for the power supply of sensors and complex monitoring systems, for example aimed at GPS localization. An initial comparative assessment of the four architectures is reported, by weighing production costs, ease of manufacture and energy harvesting performance

Augmented reality experience: from high-resolution acquisition to real time augmented contents

This paper presents results of a research project "dUcale" that experiments ICT solutions for the museum of Palazzo Ducale (Urbino). In this project, the famed painting the "Città Ideale" becomes a case to exemplify a specific approach to the digital mediation of cultural heritage. An augmented reality (AR) mobile application, able to enhance the museum visit experience, is presented. The computing technologies involved in the project (websites, desktop and social applications, mobile software, and AR) constitute a persuasive environment for the artwork knowledge. The overall goal of our research is to provide to cultural institutions best practices efficiently on low budgets. Therefore, we present a low cost method for high-resolution acquisition of paintings; the image is used as a base in AR approach. The proposed methodology consists of an improved SIFT extractor for real time image. The other novelty of this work is the multipoint probabilistic layer. Experimental results demonstrated the robustness of the proposed approach with extensive use of the AR application in front of the "Città Ideale" painting. To prove the usability of the application and to ensure a good user experience, we also carried out several users tests in the real scenario

Consumer Buying Behavior of Mobile Phone Devices

The purpose of this study is to investigate the factors affecting the decision of buying mobile phone devices in Hawassa town. In order to accomplish the objectives of the study, a sample of 246 consumers were taken by using simple random sampling technique. Both primary and secondary data were explored. Moreover, six important factors i.e. price, social group, product features, brand name, durability and after sales services were selected and analyzed through the use of correlation and multiple regressions analysis. From the analysis, it was clear that consumer’s value price followed by mobile phone features as the most important variable amongst all and it also acted as a motivational force that influences them to go for a mobile phone purchase decision. The study suggested that the mobile phone sellers should consider the above mentioned factors to equate the opportunity. Keywords: Consumer Buying Behavior, Mobile Phone, Consumer Purchase Decisio

Deep understanding of shopper behaviours and interactions using RGB-D vision

AbstractIn retail environments, understanding how shoppers move about in a store's spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers' movements, mean that accurately measuring shopper behaviour is still challenging. Over the past years, machine-learning and feature-based tools for people counting as well as interactions analytic and re-identification were developed with the aim of learning shopper skills based on occlusion-free RGB-D cameras in a top-view configuration. However, after moving into the era of multimedia big data, machine-learning approaches evolved into deep learning approaches, which are a more powerful and efficient way of dealing with the complexities of human behaviour. In this paper, a novel VRAI deep learning application that uses three convolutional neural networks to count the number of people passing or stopping in the camera area, perform top-view re-identification and measure shopper–shelf interactions from a single RGB-D video flow with near real-time performances has been introduced. The framework is evaluated on the following three new datasets that are publicly available: TVHeads for people counting, HaDa for shopper–shelf interactions and TVPR2 for people re-identification. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods (accuracy of 99.5% on people counting, 92.6% on interaction classification and 74.5% on re-id), bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications