facial recognition
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Author(s):  
Abbas Behrainwala

Abstract: In this paper a new authentication technique is discussed i-e; facial recognition verification for online voting system. It aims to develop a computerized voting system to make the election process more secure and user friendly. The electorate want to visit distinct locations like polling cubicles and stand in an extended queue to cast their vote, because of such reasons most of the people skip their chance of voting. The voter who isn't eligible also can forged its vote via way of means of faux way which can also additionally cause many problems. That's why in this project we have proposed a system or way for voting which is very effective or useful in voting. This system can also save money of the government which is spent in the election process. Overall this project is being developed to help staff of election commission of India and also reduce the human efforts. Keywords: Online Voting, Biometric Authentication, Security System.


2022 ◽  
pp. 111-124
Author(s):  
Christopher Lawless
Keyword(s):  

2022 ◽  
pp. 001872672210753
Author(s):  
Richard Weiskopf ◽  
Hans Krause Hansen

Does human reflexivity disappear as datafication and automation expand and machines take over decision-making? In trying to find answers to this question, we take our lead from recent debates about People Analytics and analyze how the use of algorithmically driven digital technologies like facial recognition and drones in work-organizations and societies at large shape the conditions of ethical conduct. Linking the concepts of algorithmic governmentality and space of ethics, we analyze how such technologies come to form part of governing practices in specific contexts. We conclude that datafication and automation have huge implications for human reflexivity and the capacity to enact responsibility in decision-making. But that itself does not mean that the space for ethical conduct disappears, which is the impression left in some literatures, but rather that is modified and (re) constituted in the interplay of mechanisms of closure (like automating decision-making, black-boxing and circumventing reflexivity), and opening (such as dis-closing contingent values and interests in processes of problematization, contestation and resistance). We suggest that future research investigates in more detail the dynamics of closure and opening in empirical studies of the use and effects of algorithmically driven digital technologies in organizations and societies.


Author(s):  
Zachary Birenbaum ◽  
Hieu Do ◽  
Lauren Horstmeyer ◽  
Hailey Orff ◽  
Krista Ingram ◽  
...  

Methods for long-term monitoring of coastal species such as harbor seals, are often costly, time-consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to identify, align and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal). We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two-years of sampling, 2019 and 2020, at seven haul-out sites in Middle Bay, we processed 1529 images representing 408 individual seals and achieved 88% (93%) rank-1 accuracy in closed set (open set) seal identification. We identified four seals that were photographed in both years at neighboring haul-out sites, suggesting that some harbor seals exhibit site fidelity within local bays across years, and that there may be evidence of spatial connectivity among haul-out sites. Using capture-mark-recapture (CMR) calculations, we obtained a rough preliminary population estimate of 4386 seals in the Middle Bay area. SealNet software outperformed a similar face recognition method developed for primates, PrimNet, in identifying seals following training on our seal dataset. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the emerging field of conservation technology.


Author(s):  
Saish Bavalekar ◽  
Ninad Gaonkar

A Smart mirror is a mirror with technology integrated with it. It uses a two-way mirror and has an inbuilt display at the back showing us different information in the form of widgets about the date, time, temperature, daily news updates. The Raspberry Pi acts as the central controller, which powers the display and collects data through sensors. The data collected is stored on cloud servers for further use. The mirror comes with facial recognition technology, which helps authenticate the user every time the user comes in the mirror range. With the help of voice commands, the mirror application can be queried to get the desired data. This automation has helped in multitasking which strives to optimize time in our daily life. In this manuscript we will review different applications of smart mirror.


Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 87
Author(s):  
Ziwei Song ◽  
Kristie Nguyen ◽  
Tien Nguyen ◽  
Catherine Cho ◽  
Jerry Gao

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.


2022 ◽  
pp. 75-80

This chapter examines the socio-political impacts of big tech in the 21st century. The chapter begins by examining the rise of big tech, and it compares the power and reach of big tech with the auto industry. The chapter next turns its attention to the concept of surveillance capitalism and reviews arguments developed by Shoshana Zuboff. Specifically, this section examines how capitalism has undergone fundamental changes in the digital age that require new responses to protect fundamental human rights. The chapter concludes by examining some of the key developments of surveillance capitalism, including facial recognition as well as government responses.


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