scholarly journals Określanie osobowości użytkowników Internetu poprzez analizowanie ich cyfrowych śladów w świetle wybranych badań dr. Michała Kosińskiego

2021 ◽  
Vol 1 (32) ◽  
pp. 99-113
Author(s):  
Michał Żytomirski
Keyword(s):  
Human Behavior ◽  
Facial Recognition ◽  
Political Orientation ◽  
Facial Images

Według danych Internet Live Stats w kwietniu 2021 roku w czasie każdej sekundy zadawano około 92 tysięcy zapytań w wyszukiwarce Google. Każda czynność dokonywana przez użytkowników urządzeń cyfrowych jest indeksowana jako tak zwane cyfrowe ślady, dzięki którym możliwe jest, przy zastosowaniu odpowiednich technologii oraz metod, precyzyjne określanie cech osobowości, poglądów politycznych oraz orientacji seksualnych tychże użytkowników. Artykuł został zainspirowany pracami dr. Michała Kosińskiego i stanowi opis problematyki związanej z analizowaniem cyfrowych śladów użytkowników Internetu (głównie mediów społecznościowych). Nadrzędnym celem artykułu jest przedstawienie badań Michała Kosińskiego i zwrócenie uwagi środowiska informatologicznego na kwestie związane z analizowaniem cyfrowych śladów użytkowników Internetu. Praca ta ma charakter popularyzatorski, nie stanowi całościowego opisu dokonań wskazanego naukowca. Artykuł nie przedstawia nowych informacji ani badań własnych – ma jednak zachęcić odbiorców do przeanalizowania literatury przedmiotu dotyczącej analizowania cyfrowych śladów oraz prywatności w dobie cyfrowej. W pracy wykorzystano metodę analizy zawartości baz danych – do zebrania i przeanalizowania literatury przedmiotu. Skupiono się na zasobach udostępnianych przez dr. Kosińskiego poprzez prywatną stronę internetową – https://www.michalkosinski.com Dodatkowo wykorzystano techniki związane z data miningiem, aby w podsumowaniu móc przedstawić archiwalne treści publikowane na stronie internetowej firmy Cambridge Analytica. Zasięg chronologiczny odszukiwanych materiałów piśmienniczych zawężono do okresu od 2011 r. do pierwszego kwartału 2021 r., skupiając się głównie na latach 2013–2021, czyli od roku opublikowania artykułu Private traits and attributes are predictable from digital records of human behavior do roku publikacji tekstu Facial recognition technology can expose political orientation from naturalistic facial images. W artykule przedstawiono wybrane prace dr. Michała Kosińskiego, które stanowią „kamienie milowe” w badaniach nad określaniem osobowości użytkowników Internetu w obrębie publikacji wskazanego autora. Co za tym idzie, w artykule nie przedstawiono tych prac, które były opisem części badań lub były przyczynkiem do podjęcia większych, dalszych pomiarów.

scholarly journals RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK PASSWORD FILE DENGAN METODE JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 21 (1) ◽  
pp. 11-21
Author(s):  
Genrawan Hoendarto ◽  
Vicni Iskandar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Data Security ◽  
Facial Recognition ◽  
Back Propagation ◽  
Computer Users ◽  
Artificial Neural ◽  
Access Rights ◽  
Facial Images

Data security for computer users is increasingly becoming a concern because it is increasingly vulnerable to illegal access even though the file has been protected with a password. This is possible with the increasing number of applications aimed at hacking owner protection. Artificial neural network that was appointed in this study is one part of computer vision, which in this study is intended to make computers able to "see" through a webcam and recognize that face has access rights to the selected file. So that computers can distinguish facial images, it needs to be trained by applying the back propagation method. The reason for choosing facial recognition is because each person has a different face, so that it can be a more effective security key than conventional methods of making or accessing files that are on a computer.

scholarly journals A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257923
Author(s):  
Sara H. Katsanis ◽  
Peter Claes ◽  
Megan Doerr ◽  
Robert Cook-Deegan ◽  
Jessica D. Tenenbaum ◽  
...  
Keyword(s):  
Medical Records ◽  
Facial Recognition ◽  
The United States ◽  
Related Research ◽  
Data Practices ◽  
Opt Out ◽  
Health Related Research ◽  
Health Related ◽  
Facial Images ◽  
Precision Health

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as “open science”; “gated science”; and “closed science.” No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

scholarly journals Comparison Between Facial Expression Recognition Algorithms - For Effective Method

2020 ◽  
pp. 146-154
Author(s):  
Sonali Singh
Keyword(s):  
Facial Expression ◽  
Human Behavior ◽  
Comparative Studies ◽  
Facial Expression Recognition ◽  
Facial Recognition ◽  
Research Paper ◽  
Expression Recognition ◽  
Recognition Algorithms

Facial expression is an ancient element for identifying humans. Human behavior, thinking or mood can be easily understood through facial expression. At present, facial expression can be evaluated by algorithm based on facial expression AI. In this paper, comparative studies have been done in methods related to facial recognition and an attempt has been made to evaluate it. Previous and recent research paper has been investigated to find out the related effective method.

scholarly journals A Study on the Laws Governing Facial Recognition Technology and Data Privacy in Malaysia

2021 ◽  
Vol 6 (10) ◽  
pp. 480-487
Author(s):  
Muhammad Ashraf Bin Mohd Nor ◽  
Mohammad Asyraf Bin Mohd Tasrib ◽  
Bryan Francis ◽  
Nurul Izzah Binti Hesham ◽  
Mohd Bahrin Bin Othman
Keyword(s):  
Data Collection ◽  
Data Privacy ◽  
Facial Recognition ◽  
Data Sets ◽  
The Past ◽  
Face Detection And Recognition ◽  
Complex Technology ◽  
Laws And Policies ◽  
Facial Images ◽  
Detection And Recognition

The advancement of technology in the past decade has led humans to achieve many great things. Among that is facial recognition technology that uses a combination of two techniques which is face detection and recognition that is capable of converting facial images of a person into readable data and connecting it with other data sets which enable it to identify, track or compare it. This study delves into the usage of facial recognition technology in Malaysia where its regulation is almost non-existent. As its usage increases, the invasive features of this technology to collect and connect its data posed a threat to the data privacy of Malaysian citizens. Due to this issue, other countries' laws and policies regarding this technology are examined and compared with Malaysia. This enables the loopholes of the current law and policies to be identified and restructured, which create a clear path on the proper regulations and changes that need to be made. Thus, this study aims to analyse the limitation of law governing data privacy and its concept in Malaysia along with changes that need to be made. This study’s finding shows the shortcoming of Malaysia’s law in governing data privacy especially when it involves complex technology that has great data collection capability like facial recognition.

scholarly journals Automatic Identification of Down Syndrome Using Facial Images with Deep Convolutional Neural Network

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 487 ◽  
Author(s):  
Bosheng Qin ◽  
Letian Liang ◽  
Jingchao Wu ◽  
Qiyao Quan ◽  
Zeyu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Down Syndrome ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Genetic Disorders ◽  
Deep Convolutional Neural Network ◽  
Automatic Identification ◽  
Deep Convolutional Neural Networks ◽  
Facial Images

Down syndrome is one of the most common genetic disorders. The distinctive facial features of Down syndrome provide an opportunity for automatic identification. Recent studies showed that facial recognition technologies have the capability to identify genetic disorders. However, there is a paucity of studies on the automatic identification of Down syndrome with facial recognition technologies, especially using deep convolutional neural networks. Here, we developed a Down syndrome identification method utilizing facial images and deep convolutional neural networks, which quantified the binary classification problem of distinguishing subjects with Down syndrome from healthy subjects based on unconstrained two-dimensional images. The network was trained in two main steps: First, we formed a general facial recognition network using a large-scale face identity database (10,562 subjects) and then trained (70%) and tested (30%) a dataset of 148 Down syndrome and 257 healthy images curated through public databases. In the final testing, the deep convolutional neural network achieved 95.87% accuracy, 93.18% recall, and 97.40% specificity in Down syndrome identification. Our findings indicate that the deep convolutional neural network has the potential to support the fast, accurate, and fully automatic identification of Down syndrome and could add considerable value to the future of precision medicine.

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