Face recognition: determine who’s in the picture.This can be used to determine facing direction or to project graphics on specific regions, like a mustache between the nose and lips. Face landmark detection: get the position and shape of the eyebrows, eyes, nose, mouth and lips, and chin.This is useful for determining where and how big the faces are in a picture.
Face detection: get the boundaries of one or multiple faces.Face-api.js can detect faces, but also estimate various things in them, as listed below. We can pass an img, canvas, or video DOM element and the library will return one or a set of results. It wraps all of this into an intuitive API. If you’re interested in the technical details of machine learning, check out “ A Primer on Machine Learning with Python”.Įnter face-api.js. However, even if we use an off-the-shelf, pre-trained model, we’d still get a little bit into the nitty-gritty of supplying the information to TensorFlow and interpreting the output. We can create, train, and use neural networks in the browser with TensorFlow.js, a popular JavaScript machine learning library. Neural networks, however, excel at these kinds of problems and can be generalized to account for most (if not all) conditions. Think of lighting conditions, facial hair, the vast variety of shapes and colors, makeup, angles, face masks, and so much more. It can be done, but to make it totally reliable is practically unachievable, given the many factors to account for. Think about it: perhaps we could write a program that scans pixels to find the eyes, nose, and mouth. Detecting objects, like a face, is quite complex.
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