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DIY Deep Learning Projects

@machinelearnbot

Two months exploring deep learning and computer vision I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this…towardsdatascience.com


What is Facial Recognition? - Applications & How it Works Lionbridge AI

#artificialintelligence

Within the field of computer vision, facial recognition is an area of research and development which deals with giving machines the ability to recognize and verify human faces. Researchers primarily work on creating face recognition technology that can improve businesses and better human lives. To help strengthen your understanding of the technology, this guide will explain what facial recognition is, how it works, its various applications, and how accurate it is today. Facial recognition software has countless applications in consumer markets, as well as the security and surveillance industries. In fact, facial recognition technology is already being used to improve security protocols and payment procedures in China, and it is possible that the rest of the world will follow suit.


50 Face Recognition APIs

@machinelearnbot

Our API provides face recognition, facial detection, eye position, nose position, mouth position, and gender classification. If you have any questions ask! Just send an email to [email protected] Happy Hacking! -Stephen Face (Detection) – A computer vision api for facial recognition and facial detection that is a perfect face.com We currently have a free api for face detection. Animetrics Face Recognition – The Animetrics Face Recognition API can be used to detect human faces in pictures.


How to build a custom face recognition dataset - PyImageSearch

#artificialintelligence

If you are already using a pre-curated dataset, such as Labeled Faces in the Wild (LFW), then the hard work is done for you. You'll be able to use next week's blog post to create your facial recognition application. But for most of us, we'll instead want to recognize faces that are not part of any current dataset and recognize faces of ourselves, friends, family members, coworkers and colleagues, etc. To accomplish this, we need to gather examples of faces we want to recognize and then quantify them in some manner. This process is typically referred to as facial recognition enrollment.