And that begins our challenge:
In law enforcement, it is extremely important to identify persons of interest quickly. In most cases, this is accomplished by showing a picture of the person to multiple law enforcement officers in hopes that someone knows the person.
In Washington County, Oregon, there are nearly 20, different bookings when a person is processed into the jail every year.
Also, in most cases, investigations move very quickly. Waiting for an officer to come on duty to identify a picture might mean missing the opportunity to solve the case.
In this post, I discuss our decision to use AWS for facial recognition. I walk through setting up web and mobile applications using AWS, demonstrating how easy it is even for someone who is new to AWS.
I then show how we used Amazon Rekognition to build a powerful tool for solving crimes. The following diagram shows the system architecture: We had not used AWS, but we had read a release announcement about Amazon Rekognition a few days prior to being approached about fixing the identification process.
We thought this would be a great product to test. Setup was fairly straightforward. In the Washington County jail management system JMSwe have an archive of mugshots going back to We needed to get the mugshots allof 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: We only wanted the front view, so we used those tags to get a list of just those. Uploading to S3 was easy. At first, we simply created the bucket and manually used the web interface to upload approximately 1, images at a time.
Implementation here be code Later, we used a script to upload the images. On the server, we use the following code to place the images in S3: After theimages were uploaded into Amazon S3, we then needed to index all of the images. In hindsight, we realized that it would have been easier to index them in the same script that I used to upload them to S3.
This would have eliminated the need to validate which images had already been indexed. To index the faces, we simply looped through every image in the bucket: It allowed me to know where in the list I was during indexing.
You do need to use the ExternalImageId property so that you know what Amazon Rekognition returns when you do a face search.
Without that, you have no back reference to the S3 object. After all of the images were indexed, we worked on a quick front end that would let me search the collection for matches when we got a new image. A simple form to a PHP script provided that front end.Darby Consulting is an IT Consulting Company specializing in IT project management and systems integration for the energy, government and education industries.
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