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Face Presentation Attack Detection a.k.a Face Antispoofing

#artificialintelligence

Face recognition is one of the most convenient biometric for access control. The wide popularity of face recognition can be attributed to ease of acquisition with cheap sensors, contact-less nature, high accuracy of algorithms, and so on. While everything is well and good there, the vulnerability to presentation attacks limits its use in safety-critical situations. Imagine your phone locked with face recognition being unlocked by someone simply showing a photo or video of you in front of the phone. There you have it, its called a presentation attack (also known as spoofing attacks).


Fawkes protects your identity from facial recognition systems, pixel by pixel

ZDNet

A new tool has been proposed for cloaking our true identities when photos are posted online to prevent profiling through facial recognition systems. Deep learning tools and facial recognition software has now permeated our daily lives. From surveillance cameras equipped with facial trackers to photo-tagging suggestions on social media, the use of these technologies is now common -- and often controversial. A number of US states and the EU are considering the ban of facial recognition cameras in public spaces. IBM has already exited the business, on the basis that the technology could end up enforcing racial bias.


Can Your Face Detector Do Anti-spoofing?

#artificialintelligence

Typical face recognition systems can be fooled by attacks as simple as a printed photograph or by playing a video recording in front of the camera. The attacks could be more sophisticated with 3D masks and involved makeup. Presentation attack detection (PAD) methods try to protect face recognition systems from these vulnerabilities, by having a separate pipeline to classify the liveness of face. The attacks can be of two types, impersonation attacks when somebody wants to be recognized as somebody else and obfuscation attacks where somebody wants to evade recognition. Most of the PAD systems involve a pipeline involving face detection, followed by some preprocessing and then the classifier which classifies the face image as real or spoof.


Disney's new AI is facial recognition for animation

#artificialintelligence

Disney's massive archive spans the course of nearly a century of content, which can turn any search for specific characters, scenes or on-screen objects within it into a significant undertaking. However, a team of researchers from Disney's Direct-to-Consumer & International Organization (DTCI) have built a machine learning platform to help automate the digital archival of all that content. They call it the Content Genome. The CG platform is built to populate knowledge graphs with content metadata, akin to what you see in Google results if you search for Steve Jobs (below). From there, AI applications can then leverage that data to enhance search, discovery and personalization features or as Anthony Accardo, Director of Research and Development at DTCI, told Engadget, help animators find specific shots and sequences from within Disney's archive.


A Beginner's Guide to Face Recognition with OpenCV in Python - Sefik Ilkin Serengil

#artificialintelligence

OpenCV becomes a de facto standard for image processing studies. The library offers some legacy techniques for face recognition as well. Local binary patterns histograms (LBPH), EigenFace and FisherFace methods are covered in the package. It is a fact that these conventional face recognition algorithms ARE NOT state-of-the-art techniques anymore. Nowadays, CNN based deep learning approaches overperform than these old-fashioned methods.


Facial Recognition Bans: What Do They Mean For AI (Artificial Intelligence)?

#artificialintelligence

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco. Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade.


Council Post: Facial Recognition Systems Security

#artificialintelligence

Facial recognition systems can be considered a controversial technology. On the one hand, this technology affects people's privacy. On the other hand, it assists in preventing or detecting violence. And now, in light of the global pandemic, it helps to deter the spread of coronavirus. Nonetheless, like any other technology, facial recognition isn't impeccable, but has vulnerabilities that make it possible to bypass a system.


AI-Powered DeepFaceDrawing Turns Sketches Into Photorealistic Portraits - The Flighter

#artificialintelligence

A research team from the Chinese Academy of Sciences and the City University of Hong Kong have unveiled DeepFaceDrawing, an AI-powered framework that turns sketches into photorealistic portraits. This deep learning system uses modules to generate the images, or in other words, it identifies the most notable facial features individually, like the eyes, nose, mouth, face shape, etc., before these vectors are merged to create realistic images. There are other deep image-to-image translation techniques that may generate face images from freehand sketches faster, but they require professional sketches or even edge maps as input. DeepFaceDrawing can implicitly model the shape space of recognizable face images and then proceeds to synthesize a face image in this space to approximate an input sketch. Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches," said researcher Shu-Yu Chen.


Facial Recognition Bans: What Do They Mean For AI (Artificial Intelligence)?

#artificialintelligence

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco. Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade.


Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning

#artificialintelligence

Let's tackle this problem one step at a time. For each step, we'll learn about a different machine learning algorithm. I'm not going to explain every single algorithm completely to keep this from turning into a book, but you'll learn the main ideas behind each one and you'll learn how you can build your own facial recognition system in Python using OpenFace and dlib. The first step in our pipeline is face detection. Obviously we need to locate the faces in a photograph before we can try to tell them apart!