Amazon Rekognition

Rekognition can to be "trained" for specialized activities, but the algorithm is ready to go "out-of-the-box" with the following functionality:

• Facial recognition - this often the first thing people think of when they think of machines doing image analysis.
• Celebrity recognition - in addition to identifying faces, the algorithm knows "celebrities" out of the box. Jeff Bezos or Werner Vogels are perfect examples.
• Sentiment analysis - "that's a face, are the people happy or sad", etc?
• Labeling - one of the most powerful features of Rekognition, the machine can label images with things it observes like:
  • Activities (running, playing, laughing, etc.)
  • Objects (trees, streets, cars, people, etc.)
  • Scenes (beach, sunset, city, etc.)
  • Concepts (outdoors, fun, etc.)
• Person pathing - given a person, the algorithm can track the path that person moves.
• Text detection - the machine can read things like menus, t-shirts, etc.
• Content moderation - out of the box, the machine can detect things like pornography, violence, foul language, etc. to automate the process of content moderation.
• Personal Protective Equipment (PPE) - Rekognition can detect the usage of PPE

Rekognition is a visual machine learning algorithm, but it also consumes video. This means it also listens to and can label the audio as well, an important part of content moderation.

While visual machine learning can be complicated to setup, the infrastructure costs on Amazon are often negligible. For example:

• Image analysis generally ranges between $0.001 per image (for the first million images) up to $0.00025 per image if you are processing 35 million images or more.
• Basic label detection starts at $0.0010 per image, aka.1,000 images for 1 dollar.
• Video analysis is generally prices per minute per service, with all of the following services billed at $0.10/min: label detection, content moderation, text detection, face detection, celebrity recognition, face search, person pathing.

More robust pricing can be found on the Amazon Rekognition pricing page, but even if the calculations get complicated, the price is still incredibly low.

Out of the box functionality in deep learning algorithms is great, but it can also be extended. Most of this functionality boils down to custom labeling but this process is exceedingly powerful. Here are a few ideas:

• It could be trained to recognize specific people, not just celebrities.
• It could be trained to recognize clothing styles.
• It could learn when pets are misbehaving.

The possibilities are almost endless, and we are just starting to see more and more creative application. As with any algorithm, training can be complicated but if you have an idea we'd be happy to talk with you about it.