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

Biases in Tree-Based Models

Nina Zumel looks at tree-based ensembling models like random forest and gradient boost and shows that they can be biased: In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data […]

Read More

Comparing Poisson Regression to Regressing Against Logs

Nina Zumel compares a pair of methods for performing regression when income is the dependent variable: Regressing against the log of the outcome will not be calibrated; however it has the advantage that the resulting model will have lower relative error than a Poisson regression against income. Minimizing relative error is appropriate in situations when […]

Read More

Categories

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