Facial Recognition With Amazon Rekognition

Chris Adzima describes how his law enforcement agency uses Amazon Rekognition for facial recognition:

Setup was fairly straightforward. In the Washington County jail management system (JMS), we have an archive of mugshots going back to 2001. We needed to get the mugshots (all 300,000 of them) into Amazon S3. Then we need to index them all in Amazon Rekognition, which took about 3 days.

Our JMS allows us to tag the shots with the following information: front view or side view, scars, marks, or tattoos. We only wanted the front view, so we used those tags to get a list of just those.

Read on for sample implementation details, including moving images to S3, building the facial recognition “database,” and using it.

Related Posts

Defining TF-IDF

Bruno Stecanella explains the concept behind TF-IDF: TF-IDF was invented for document search and information retrieval. It works by increasing proportionally to the number of times a word appears in a document, but is offset by the number of documents that contain the word. So, words that are common in every document, such as this, what, and if, rank […]

Read More

Sentiment Analysis with Python

Bruno Stecanella shows us how to use MonkeyLearn to perform sentiment analysis in Python: Sentiment analysis is a set of Natural Language Processing (NLP) techniques that takes a text (in more academic circles, a document) written in natural language and extracts the opinions present in the text. In a more practical sense, our objective here is to take a text […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930