Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation

Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed to aid in these investigations to expedite this evidence discovery process, and decrease investigator exposure to traumatic material. Automated techniques also show promise in decreasing the overflowing backlog of evidence obtained from increasing numbers of devices and online services. A lack of sufficient training data combined with natural human variance has been long hindering accurate automated age estimation -- especially for underage subjects. This paper presented a comprehensive evaluation of the performance of two cloud age estimation services (Amazon Web Service's Rekognition service and Microsoft Azure's Face API) against a dataset of over 21,800 underage subjects. The objective of this work is to evaluate the influence that certain human biometric factors, facial expressions, and image quality (i.e. blur, noise, exposure and resolution) have on the outcome of automated age estimation services. A thorough evaluation allows us to identify the most influential factors to be overcome in future age estimation systems

How old was she? The accuracy of assessing the age of adolescents' based on photos

Information on the degree to which individuals can make accurate estimations of someone's age can be of importance in different legal contexts, such as for example child sexual abuse cases in which the victim is an adolescent and asylum procedures. There is, however, a scarcity of studies concerning age estimations conducted with young target persons. Using facial photos of target persons aged 12-18 years, we investigated the effects of gender, age, and ethnicity of both targets (n = 240) and observers (n = 869) on the accuracy of age estimation. We also investigated the effects of targets' facial expressions (neutral or smiling), use of makeup, and photo quality. Participants overestimated the age of the adolescents by, on average, 3.51 years. Participants overestimated the age of young adolescent girls to a greater extent than that of younger boys. Men made larger overestimations than women. Participants also estimated smiling targets as being older than targets with neutral facial expression, and the age of girls with makeup to be older than girls without makeup. Because there was considerable variation in the accuracy of estimations, and overestimates were common, we conclude that the ability of individuals to estimate the age of adolescents is generally low. This might have important legal implications.Peer reviewe

Aging traits and sustainable trophy hunting of African lions

Trophy hunting plays a significant role in wildlife conservation in some contexts in various parts of the world. Yet excessive hunting is contributing to species declines, especially for large carnivores. Simulation models suggest that sustainable hunting of African lions may be achieved by restricting offtakes to males old enough to have reared a cohort of offspring. We tested and expanded criteria for an age-based approach for sustainably regulating lion hunting. Using photos of 228 known-age males from ten sites across Africa, we measured change in ten phenotypic traits with age and found four age classes with distinct characteristics: 1-2.9 years, 3-4.9 years, 5-6.9 years, and ≥7 years. We tested the aging accuracy of professional hunters and inexperienced observers before and after training on aging. Before training, hunters accurately aged more lion photos (63%) than inexperienced observers (48%); after training, both groups improved (67-69%). Hunters overestimated 22% of lions <5 years as 5-6.9 years (unsustainable) but only 4% of lions <5 years as ≥7 years (sustainable). Due to the lower aging error for males ≥7 years, we recommend 7 years as a practical minimum age for hunting male lions. Results indicate that age-based hunting is feasible for sustainably managing threatened and economically significant species such as the lion, but must be guided by rigorous training, strict monitoring of compliance and error, and conservative quotas. Our study furthermore demonstrates methods for identifying traits to age individuals, information that is critical for estimating demographic parameters underlying management and conservation of age-structured species.http://www.elsevier.com/ locate/biocon2017-09-30hb2016Centre for Wildlife ManagementMammal Research InstituteZoology and Entomolog

Privaatsust säilitava raalnägemise meetodi arendamine kehalise aktiivsuse automaatseks jälgimiseks koolis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKuidas vaadelda inimesi ilma neid nägemata? Öeldakse, et ei ole viisakas jõllitada. Õigus privaatsusele on lausa inimõigus. Siiski on inimkäitumises palju sellist, mida teadlased tahaksid uurida inimesi vaadeldes. Näiteks tahame teada, kas lapsed hakkavad vahetunnis rohkem liikuma, kui koolis keelatakse nutitelefonid? Selle välja selgitamiseks peaks teadlane küsima lapsevanematelt nõusolekut võsukeste vaatlemiseks. Eeldusel, et lapsevanemad annavad loa, oleks klassikaliseks vaatluseks vaja tohutult palju tööjõudu – mitu vaatlejat koolimajas iga päev piisavalt pikal perioodil enne ja pärast nutitelefoni keelu kehtestamist. Doktoritööga püüdsin lahendada korraga privaatsuse probleemi ja tööjõu probleemi, asendades inimvaatleja tehisaruga. Kaasaegsed masinõppe meetodid võimaldavad luua mudeleid, mis tuvastavad automaatselt pildil või videos kujutatud objekte ja nende omadusi. Kui tahame tehisaru, mis tunneb pildil ära inimese, tuleb moodustada masinõppe andmestik, kus on pilte inimestest ja pilte ilma inimesteta. Kui tahame tehisaru, mis eristaks videos madalat ja kõrget kehalist aktiivsust, on vaja vastavat videoandmestikku. Doktoritöös kogusingi andmestiku, kus video laste liikumisest on sünkroniseeritud puusal kantavate aktseleromeetritega, et treenida mudel, mis eristaks videopikslites madalamat ja kõrgemat liikumise intensiivsust. Koostöös Tehonoloogiainstituudi iCV laboriga arendasime välja videoanalüüsi sensori prototüübi, mis suudab reaalaja kiirusel hinnata kaamera vaateväljas olevate inimeste kehalise aktiivsuse taset. Just see, et tehisaru suudab tuletada videost kehalise aktiivsuse informatsiooni ilma neid videokaadreid salvestamata ega inimestele üldsegi näitamata, võimaldab vaadelda inimesi ilma neid nägemata. Väljatöötatud meetod on mõeldud kehalise aktiivsuse mõõtmiseks koolipõhistes teadusuuringutes ning seetõttu on arenduses rõhutatud privaatsuse kaitsmist ja teaduseetikat. Laiemalt vaadates illustreerib doktoritöö aga raalnägemistehnoloogiate potentsiaali töötlemaks visuaalset infot linnaruumis ja töökohtadel ning mitte ainult kehalise aktiivsuse mõõtmiseks kõrgete teaduseetika kriteerimitega. Siin ongi koht avalikuks aruteluks – millistel tingimustel või kas üldse on OK, kui sind jõllitab robot?  How to observe people without seeing them? They say it's not polite to stare. The right to privacy is considered a human right. However, there is much in human behavior that scientists would like to study via observation. For example, we want to know whether children will start moving more during recess if smartphones are banned at school? To figure this out, scientists would have to ask parental consent to carry out the observation. Assuming parents grant permission, a huge amount of labour would be needed for classical observation - several observers in the schoolhouse every day for a sufficiently long period before and after the smartphone ban. With my doctoral thesis, I tried to solve both the problem of privacy and of labor by replacing the human observer with artificial intelligence (AI). Modern machine learning methods allow training models that automatically detect objects and their properties in images or video. If we want an AI that recognizes people in images, we need to form a machine learning dataset with pictures of people and pictures without people. If we want an AI that differentiates between low and high physical activity in video, we need a corresponding video dataset. In my doctoral thesis, I collected a dataset where video of children's movement is synchronized with hip-worn accelerometers to train a model that could differentiate between lower and higher levels of physical activity in video. In collaboration with the ICV lab at the Institute of Technology, we developed a prototype video analysis sensor that can estimate the level of physical activity of people in the camera's field of view at real-time speed. The fact that AI can derive information about physical activity from the video without recording the footage or showing it to anyone at all, makes it possible to observe without seeing. The method is designed for measuring physical activity in school-based research and therefore highly prioritizes privacy protection and research ethics. But more broadly, the thesis illustrates the potential of computer vision technologies for processing visual information in urban spaces and workplaces, and not only for measuring physical activity or adhering to high ethical standards. This warrants wider public discussion – under what conditions or whether at all is it OK to have a robot staring at you?https://www.ester.ee/record=b555972

Fetal paleopathology: an impossible discipline?

This chapter introduces the concept of fetal paleopathology in archaeological material, highlighting the limitations and potential of such research to inform us about the lives of mothers and their babies in the past. Problems with terminology, aging methods, preservation and recognizing lesions in skeletal remains are discussed, before potential new sources of research are highlighted

Design and development of a students' performance predicting LMS utilizing Machine Learning based on mental stress level measured through a Bluetooth enabled smart watch

Stress and academic anxiety problems can negatively impact numerous aspects of students lives, resulting in degrading their academic achievement, quality of life, and social behaviour. Various research suggests that depression is associated with lower academic performance of students. The aim of this research is twofold. Firstly, in order to establish a correlation between students mental stress level and their academic performance, a dataset has been compiled through gathering the data by conducting a survey in a university located in Punjab, Pakistan. The questionnaires were based on measuring the stress level of students using Perceived Stress Scale (PSS) , Cognitive performance assessment scale, in addition to some other demographic questions. Afterwards, this dataset has been analysed utilizing various machine learning algorithms. The second objective was to develop an innovative, affordable and smart performance predicting Learning Management System that takes into account students mental stress while predicting the students performance using machine learning models. The technique that was used for the mental stress measurements of the students was based on a phenomenon known as the Heart Rate Variability (HRV). A smart watch was utilized to measure the Heart Rate Variability of the students that was used to assess the stress level of students in academics. A Machine Learning (ML) model was trained using various parameters that were derived from the Heart Rate Variability. The original dataset that was used to train the model is known as Swell dataset. The SWELL dataset consists of HRV indices computed from the multimodal SWELL knowledge work dataset for research on stress and user modelling. The ML model effectively made prediction about the stress levels of the students with an accuracy of 98.1%.Objectius de Desenvolupament Sostenible::3 - Salut i BenestarObjectius de Desenvolupament Sostenible::4 - Educació de Qualita

Early maturation and substance use across adolescence and young adulthood : A longitudinal study of Finnish twins

Early maturation, indexed by pubertal development (PD), has been associated with earlier initiation and greater frequency of adolescent substance use, but this relationship may be biased by confounding factors and effects that change across development. Using a population-based Finnish twin sample (N = 3,632 individuals), we conducted twin modeling and multilevel structural equation modeling of the relationship between PD and substance use at ages 12-22. Shared environmental factors contributed to early PD and heavier substance use for females. Biological father absence was associated with early PD for boys but not girls, and did not account for the relationship between PD and substance use. The association between early PD and heavier substance use was partially due to between-family confounds, although early PD appeared to qualitatively alter long-term trajectories for some substances (nicotine), but not others (alcohol). Mediation by peer and parental factors did not explain this relationship within families. However, higher peer substance use and lower parental monitoring were themselves associated with heavier substance use, strengthening the existing evidence for these factors as targets for prevention/intervention efforts. Early maturation was not supported as a robust determinant of alcohol use trajectories in adolescence and young adulthood, but may require longer term follow-up. Subtle effects of early PD on nicotine and illicit drug use trajectories throughout adolescence and adulthood merit further investigation.Peer reviewe

Age estimation in the living : a test of 6 radiographic methods

There is a growing recognition that there is a requirement for methods of age estimation of the living to be rigorously tested to ensure that they are accurate, reliable and valid for use in forensic and humanitarian age estimation. The necessity for accurate and reliable methods of age estimation are driven both by humanitarian, political and judicial need. Age estimation methods commonly in use today are based on the application of reference standards, known as atlases, which were developed using data collected from children who participated in longitudinal studies in the early to mid-1900s. The standards were originally developed to provide a baseline to which radiographs could be compared in order to assess the child’s stage of skeletal development in relation to their chronological age, a purpose for which they are still utilised in the medical community. These atlases provide a testable link between skeletal age and chronological age which has been recognised by forensic practitioners who have essentially hijacked this medical capability and applied it to their fields. This has resulted in an increased use of these standards as a method of predicting the chronological age from the skeletal age of a child when the former is unknown. This novel use of the atlases on populations who are distinct, ethnically, temporally and geographically, from those whose data was gathered and was used in the design of the standard leaves the forensic outcomes vulnerable to challenge in court. This study aims to examine the reliability and accuracy of these standards in relation to a modern population, providing a sound statistical base for the use of these standards for forensic purposes. Radiographs were collected from the local hospital from children who had been X-rayed for investigation during attendance at the local A&E department. Four body areas were selected for investigation; the hand-wrist, the elbow, the knee and the foot-ankle and tests were undertaken to assess the radiographs using six commonly uses methods of age estimation. Further images of the wrist and elbow were collected from children in New Delhi, India. These images were subject to age estimation utilising the methods described.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Age estimation in the living : a test of 6 radiographic methods

There is a growing recognition that there is a requirement for methods of age estimation of the living to be rigorously tested to ensure that they are accurate, reliable and valid for use in forensic and humanitarian age estimation. The necessity for accurate and reliable methods of age estimation are driven both by humanitarian, political and judicial need. Age estimation methods commonly in use today are based on the application of reference standards, known as atlases, which were developed using data collected from children who participated in longitudinal studies in the early to mid-1900s. The standards were originally developed to provide a baseline to which radiographs could be compared in order to assess the child’s stage of skeletal development in relation to their chronological age, a purpose for which they are still utilised in the medical community. These atlases provide a testable link between skeletal age and chronological age which has been recognised by forensic practitioners who have essentially hijacked this medical capability and applied it to their fields. This has resulted in an increased use of these standards as a method of predicting the chronological age from the skeletal age of a child when the former is unknown. This novel use of the atlases on populations who are distinct, ethnically, temporally and geographically, from those whose data was gathered and was used in the design of the standard leaves the forensic outcomes vulnerable to challenge in court. This study aims to examine the reliability and accuracy of these standards in relation to a modern population, providing a sound statistical base for the use of these standards for forensic purposes. Radiographs were collected from the local hospital from children who had been X-rayed for investigation during attendance at the local A&E department. Four body areas were selected for investigation; the hand-wrist, the elbow, the knee and the foot-ankle and tests were undertaken to assess the radiographs using six commonly uses methods of age estimation. Further images of the wrist and elbow were collected from children in New Delhi, India. These images were subject to age estimation utilising the methods described.EThOS - Electronic Theses Online ServiceGBUnited Kingdo