Facebook could one day build facial gesture controls for its app thanks to the acquisition of a Carnegie Mellon University spinoff company called FacioMetrics. The startup made an app called Intraface that could detect seven different emotions in people's faces, but it's been removed from the app stores. The acquisition aligns with a surprising nugget of information Facebook slipped into a 32-bullet point briefing sent to TechCrunch this month. "Future applications of deep learning platform on mobile: Gesture-based controls, recognize facial expressions and perform related actions" It's not hard to imagine Facebook one day employing FacioMetrics' tech and its own AI to let you add a Like or one of its Wow/Haha/Angry/Sad emoji reactions by showing that emotion with your face. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways.
Who knows in future you may be able to emote to your friend's FB posts in your own wide mouthed haha, open mouthed wow or a puckered brow frown? Going by the indications, things are inching towards such a reality. Facebook's latest acquisition – of a face recognition company FacioMetrics -- has become the talk of the tech town regarding the possibilities of inclusion of facial gesture controls on the app front. Close on the heels of this merger, this startup's apps have been withdrawn from the App Store and Play Store. 'Intraface', the facial image analysis app from Faciometrics could enable detection of seven facial emotions.
Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.
Update: Machine Learning is Fun! Part 5 is now available! Also, don't forget to check out Part 1, Part 2 and Part 3. Have you noticed that Facebook has developed an uncanny ability to recognize your friends in your photographs? In the old days, Facebook used to make you to tag your friends in photos by clicking on them and typing in their name. This technology is called face recognition.