MobiBits: Multimodal Mobile Biometric Database

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201

Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.Comment: Accepted into ICCBS 202

MeciFace: Mechanomyography and Inertial Fusion based Glasses for Edge Real-Time Recognition of Facial and Eating Activities

The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective monitoring systems. In this paper, we present MeciFace, an innovative wearable technology designed to monitor facial expressions and eating activities in real-time on-the-edge (RTE). MeciFace aims to provide a low-power, privacy-conscious, and highly accurate tool for promoting healthy eating behaviors and stress management. We employ lightweight convolutional neural networks as backbone models for facial expression and eating monitoring scenarios. The MeciFace system ensures efficient data processing with a tiny memory footprint, ranging from 11KB to 19KB. During RTE evaluation, the system achieves impressive performance, yielding an F1-score of < 86% for facial expression recognition and 90% for eating/drinking monitoring, even for the RTE of an unseen user.Comment: Submitted to Nature Scientific Report

Hedonic and utilitarian attitudes towards technology and innovation : purchase Intentions of Audio Devices : the AirPods Case

The substantial technological growth that defines the era we are living in, involves not only improvement of current electronics features, but also launches of innovative products in the market, and both situations from the consumer electronics segment hold an uncertainty regarding the new product acceptance. As to minimize this uncertainty, brands must be aware of preferences and characteristics which drive consumers to their products, addressing properly the potential key target groups. This dissertation relies on identifying relevant dissimilarities across hedonic and utilitarian behaviour profiles concerning the smartphone consumption, the attributes most valued in electronics, and assess whether these profiles influence the consumers’ attitudes towards technology and innovation in general. On a further analysis, the research is applied to the practical case of AirPods, the new wireless earbuds from Apple, limited to the Portuguese market. From the outcomes of the study it was possible to properly differentiate two groups of smartphone users related to hedonic and utilitarian behaviours. The main findings suggest that these consumer profiles do not constitute the main driver in influencing attitudes towards technology and innovation, although they disclose a relative impact on purchase intentions of the innovative audio device from Apple. Nonetheless, demographic aspects, such as age and gender, are also highlighted due to the relatively influence they have in these attitudes. The collected analyzed data might also contribute for brand managers to better communicate to their target groups, enhancing in their products the ‘emotional side’ for the hedonics and the ‘feature side’ for the utilitarians.O crescimento tecnológico substancial que define a era em que vivemos, envolve não só uma melhoria das atuais características eletrónicas, mas também lançamentos de produtos inovadores no mercado, tendo em conta que ambas as situações no segmento de produtos de eletrónica detêm uma incerteza associada à aceitação do novo produto. Para minimizar esta incerteza, as marcas devem estar atentas às preferências e características que levam os consumidores até aos seus produtos, de forma a atrair os potenciais grupos target desejados. Esta dissertação centra-se em identificar diferenças significativas entre os perfis de comportamento hedónico e utilitário relativamente a smartphones, os atributos mais valorizados em eletrónica, e verificar se estes perfis influenciam as atitudes dos consumidores perante tecnologia e inovação em geral. Numa análise mais profunda, o estudo é aplicado ao caso prático dos AirPods, os novos phones wireless da Apple, limitado ao mercado português. Dos resultados foi possível diferenciar dois grupos de utilizadores de smartphone relacionados com comportamentos hedónicos e utilitários. As principais conclusões sugerem que estes perfis comportamentais não constituem o fator principal em determinar atitudes perante tecnologia e inovação, apesar de revelarem um relativo impacto em intenções de compra do inovador acessório de áudio da Apple. No entanto, aspetos demográficos, como idade e género, destacam-se também devido ao seu relativo impacto nas atitudes em questão. A informação analisada poderá fornecer dados aos gestores de marca para comunicarem efetivamente aos seus grupos target, evidenciando nos seus produtos o ‘lado emocional’ para os hedónicos e o ‘lado técnico’ para os utilitários

Spartan Daily, June 18, 1936

Volume 24, Issue 157https://scholarworks.sjsu.edu/spartandaily/2485/thumbnail.jp

Text-Guided Generation and Editing of Compositional 3D Avatars

Our goal is to create a realistic 3D facial avatar with hair and accessories using only a text description. While this challenge has attracted significant recent interest, existing methods either lack realism, produce unrealistic shapes, or do not support editing, such as modifications to the hairstyle. We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories. Our observation is that the hair and face, for example, have very different structural qualities that benefit from different representations. Building on this insight, we generate avatars with a compositional model, in which the head, face, and upper body are represented with traditional 3D meshes, and the hair, clothing, and accessories with neural radiance fields (NeRF). The model-based mesh representation provides a strong geometric prior for the face region, improving realism while enabling editing of the person's appearance. By using NeRFs to represent the remaining components, our method is able to model and synthesize parts with complex geometry and appearance, such as curly hair and fluffy scarves. Our novel system synthesizes these high-quality compositional avatars from text descriptions. The experimental results demonstrate that our method, Text-guided generation and Editing of Compositional Avatars (TECA), produces avatars that are more realistic than those of recent methods while being editable because of their compositional nature. For example, our TECA enables the seamless transfer of compositional features like hairstyles, scarves, and other accessories between avatars. This capability supports applications such as virtual try-on.Comment: Home page: https://yfeng95.github.io/tec

Methods for monitoring the human circadian rhythm in free-living

Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, we estimate light exposure from smartphone sensor and weather in- formation. We applied the Kronauer model to compare the phase shift resulting from head light measurements, wrist measurements, and smartphone estimations. We found it was possible to monitor circadian phase shift from light estimation based on smartphone sensor and weather information with a weekly error of 32±17min, which outperformed wrist measurements in 11 out of 12 participants. Sleep could be detected from smartphone use with an onset error of 40±48 min and wake error of 42±57 min. Screen use could be detected smart eyeglasses with 0.9 ROC AUC for ambient light intensities below 200lux. Nine clusters of ADLs were distinguished using Gaussian mixture models with an average accuracy of 77%. In conclusion, a combination of the proposed smartphones and smart eyeglasses applications could support users in synchronising their circadian clock to the external clocks, thus living a healthier lifestyle