Finding Meaning in Snapchat Images, One Billion at a Time
February 27, 2017
The article on InfoQ titled Amazon Introduces Rekognition for Image Analysis explores the managed service aimed at the explosive image market. According to research cited in the article, over 1 billion photos are taken every single day on Snapchat alone, compared to the 80 billion total taken in the year 2000. Rekognition’s deep learning power is focused on identifying meaning in visual content. The article states,
The capabilities that Rekognition provides include Object and Scene detection, Facial Analysis, Face Comparison and Facial Recognition. While Amazon Rekognition is a new public service, it has a proven track record. Jeff Barr, chief evangelist at AWS, explains: Powered by deep learning and built by our Computer Vision team over the course of many years, this fully-managed service already analyzes billions of images daily. It has been trained on thousands of objects and scenes. Rekognition was designed from the get-go to run at scale.
The facial analysis features include markers for image quality, facial landmarks like facial hair and open eyes, and sentiment expressed (smiling = happy.) The face comparison feature includes a similarity score that estimates the likelihood of two pictures being of the same person. Perhaps the most useful feature is object and scene detection, which Amazon believes will help users find specific moments by searching for certain objects. The use cases also span vacation rental markets and travel sites, which can now tag images with key terms for improved classifications.
Chelsea Kerwin, February 27, 2017
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