This blog is syndicated from The New Rules of Privacy: Building Loyalty with Connected Consumers in the Age of Face Recognition and AI. To learn more click here. Since the invention of face recognition in the 1960s, has any single technology sparked more fascination for public safety officials, companies, journalists and Hollywood? When people learn that I'm the CEO of a face recognition company, they commonly reference its fictional use in shows like CSI, Black Mirror or even films such as the 1980s James Bond movie A View to a Kill. Most often, however, they mention Minority Report starring Tom Cruise.
Tech and entertainment companies are betting big on facial recognition technology and Disney wants to be the cool kid on the block. SEE ALSO: Disney unveils'Star Wars Land' and it is everything fans dreamed of The company's research team is using deep learning techniques to track the facial expressions of an audience watching movies in order to asses their emotional reactions to it. Called "factorised variational autoencoders" (FVAEs), the new algorithm is so sharp that is reportedly able to predict how a member of the audience will react to the rest of a film after analysing their facial expressions for just 10 minutes. In a more sophisticated version to recommendation systems for online shopping used by Amazon -- which suggests new products based on your shopping history -- the FVAEs recognise a series of facial expressions from the audience, such as smiles and laughter. Then, they make connections between viewers to see if a certain movie is getting the wanted reactions at the right place and time.
DIS is known for its box office hits: Beauty and the Beast, Rogue One: A Star Wars Story, and Captain America: Civil War, just to name a few. As one of the biggest media conglomerates in the world, Disney is looking to better understand its moviegoing audience so that its upcoming movie line-up can continue to be moneymakers and crowd pleasers. Disney hopes to do this through artificial intelligence (AI) and facial recognition technology, using deep learning techniques to track the facial expressions of an audience watching a movie in order to gauge any emotional reaction to it. Called "factorized variational autoencoders," or FVAEs, the researchers said the technology works so well that after observing an audience member's face for just 10 minutes, it can predict how the person will react to the rest of the movie. The FVAEs go on to then recognize many facial expressions from movie viewers on their own, like smiles and laughter, and can make connections between different viewers to see if a particular movie is getting a wanted reaction at the right place and time.
Deep learning is increasingly capable of assessing the emotion of human faces, looking across an image to estimate how happy or sad the people in it appear to be. What if this could be applied to television news, estimating the average emotion of all of the human faces seen on the news over the course of a week? While AI-based facial sentiment assessment is still very much an active area of research, an experiment using Google's cloud AI to analyze a week's worth of television news coverage from the Internet Archive's Television News Archive demonstrates that even within the limitations of today's tools, there is a lot of visual sentiment in television news. To better understand the facial emotion of television, CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news, were analyzed using Google's Vision AI image understanding API with all of its features enabled, including facial detection. Facial detection is very different from facial recognition.
In today's blog post you are going to learn how to perform face recognition in both images and video streams using: As we'll see, the deep learning-based facial embeddings we'll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We'll start with a brief discussion of how deep learning-based facial recognition works, including the concept of "deep metric learning". From there, I will help you install the libraries you need to actually perform face recognition. Finally, we'll implement face recognition for both still images and video streams.