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Face Recognition: Instructional Materials

Facebook issues $397 checks to Illinois residents as part of class-action lawsuit


More than a million Illinois residents will receive a $397 settlement payment from Facebook this week, thanks to a legal battle over the platform's since-retired photo-tagging system that used facial recognition. It's been nearly seven years since the 2015 class-action lawsuit was first filed, which accused Facebook of breaking a state privacy law that forbids companies from collecting biometric data without informing users. The platform has since faced broad, global criticism for its use of facial recognition tech, and last year Meta halted the practice completely on Facebook and Instagram. But as Vox notes, the company has made no promises to avoid facial recognition in future products. Even though it was first filed in Illinois, the class-action lawsuit eventually wound up on Facebook's home turf -- at the U.S. District Court for Northern California.

What if Zoom Could Read Your Facial Expression?


Right now, companies are developing and selling AI products intended to tell your boss, or your teacher, how you're feeling while on camera. Emotion AI is supposedly capable of taking our expressions and micro-expressions, capturing them via computer vision, and then spitting out some sort of score that says whether someone's engaged with what's being said in a virtual classroom or even responding well to a sales pitch. But what's unclear is how well--or whether--it really works. On Friday's episode of What Next: TBD, I spoke with Kate Kaye, a reporter for Protocol, about whether AI really know what you're feeling. Our conversation has been edited and condensed for clarity.

Army Testing Facial Recognition in Child-Care Centers


Live video feeds of daycare centers are common, but the Army wants to take their kid-monitoring capabilities to the next level. Under a new pilot program being rolled out at a Fort Jackson, S.C. child-care center, the military is looking for service providers to layer commercially available facial recognition and artificial intelligence (AI) over existing closed-circuit television video feeds to improve childcare and cut costs. The request for bids on the project, called Installations of the Future: Technology Pilot for Child Development Center, explained that the CCTV feeds aren't constantly monitored by humans and the pilot program will explore whether AI could fill in the gaps. "Video analytic software provides the added security of continual computer monitoring used as an addition to the human CCTV monitoring," the request for bid said. "Moreover, it provides instant notifications to staff on a wide range of important AR 190-3 monitoring parameters as events occur."

Computer Vision Masterclass


Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques.

How to install the iOS 15.4 public beta


There have been some exciting features announced for the next version of Apple's mobile operating system, iOS 15.4, including the ability to use Face ID with a mask and to access some snazzy new emoji. If you can't wait, you can test them in the first available public beta right now -- assuming you're willing to risk encountering possible bugs. Here, we walk you through the steps for installing the software. Before we get started, here's the usual word of warning about installing unfinished beta software: these releases may seem stable for general use, but they could contain some bugs. Your experience may differ from others, depending on the apps you use.

Introduction to Face Recognition


This article corresponds to the class notes about Face Recognition taken by me on the Convolutional Neural Networks Andre Ng's 4th course of the Deep Learning Specialization of Hope you find this material helpful. In the Face recognition literature, people often talk about face verification and recognition. How could we define these problems? Given an input image as well as name or ID, and the job of the system is to verify whether or not the input image is that of the claimed person, also called a one to one problem.

Conversational Agents: Theory and Applications Artificial Intelligence

In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain. We also consider the concept of embodied conversational agents, briefly reviewing aspects such as character animation and speech processing. The many different approaches for representing dialogue in CAs are discussed in some detail, along with methods for evaluating such agents, emphasizing the important topics of accountability and interpretability. A brief historical overview is given, followed by an extensive overview of various applications, especially in the fields of health and education. We end the chapter by discussing benefits and potential risks regarding the societal impact of current and future CA technology.

#003 Advanced Computer Vision - Multi-Task Cascaded Convolutional Networks


Highlights: Face detection and alignment are correlated problems. Change in various poses, illuminations, and occlusions in unrestrained environments can make these problems even more challenging. In this tutorial, we will study how deep learning approaches can be great performing solutions for these two problems. We will study a deep cascaded multi-task framework proposed by Kaipeng Zhang [1] et al. that predicts face and landmark location in a coarse-to-fine manner. Recognizing faces and expressions involves crucial face detection and alignment solutions.



Then we will set up the prerequisite for them. Later we will proceed with face detection using MTCNN and preprocessing of the detected face for recognition.

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