So now let's talk about Amazon Rekognition.
And Rekognition is a service used to find objects, people,
texts, scenes in images and videos using machine learning.
So it can do facial analysis
and facial search to do user verification
and count how many people there are
in an image, for example.
Or you can create your own database of familiar faces
or you can compare against a database
of celebrities to find people in your images as well.
So the use cases for Rekognition is to take your image
and do labeling images and video
and to labeling, content moderation, text detection,
face detection and analysis,
for example, find the gender, the age range
and the emotions that are associated with these faces,
do a face search and verification,
do celebrity recognition,
as well as pathing.
So for example, if you're doing a sports game analysis
of a video, so that's a high level.
Remember Rekognition is about images and videos.
So on the Rekognition website
we can see we can automate our image and video analysis
with machine learning.
And I really like this website
because it shows you how it works.
So for example,
for this image we can identify the elements of this image.
For example, a person, a rock, a mountain bike,
a crest and outdoors.
We can label, for example, what we see in images,
for example, golden retrievers or dogs.
Then I can look at content moderation
to make sure that it's appropriate for all ages.
I can detect text, for example, for a run.
We want to see the numbers of each runner in the run.
We can do face detection analysis,
for example, this person looks happy.
She's smiling,
her eyes are open and she's a female.
Face search and verification,
maybe if you have a security application.
Finally, if you want to recognize a celebrity,
you can take a picture of them.
And this is the CTO of AWS.
And then finally, pathing,
for example, if you're monitoring a soccer game,
you could see where everyone is going to do
maybe some real time analytics.
And there's one feature you need to know about
going into the exam.
It's around content moderation.
So this is used to detect content that is inappropriate,
unwanted, or offensive in images and videos.
So this will be used, for example,
if you have a social network or if you broadcast media
or if you do advertising or if you're doing e-commerce
and you need to create a safe user experience
and make sure the image is displayed,
don't show any kind of content that would be
deemed offensive by some people.
For example, racist content
or pornography or other kind of things like this.
So how does that work?
Well, the image will be analyzed by Amazon Rekognition
and then you set a Minimum Confidence Threshold
for items that will be flagged.
So you set it to whatever percentage you want.
And obviously the lower you set this percentage
the more matches you're going to get.
And this confidence percentage represents how
confident Amazon Rekognition is
that this flagged image represents indeed
some inappropriate or offensive character.
And then once you have done this
and you flagged some images,
you may want to do a human manual review.
And so to do so,
you can use something called Amazon Augmented AI
called A2I
and you do optional manual review
directly in Amazon A2I.
And all of this process right here allows you to
automatically flag images that can be sensitive
and then use a final manual review
to know whether or not you want to keep them or delete them.
And this can help you with comply
with regulations in case you must detect
these kind of content
before they're posted to your applications.
So that's it for this lecture.
I hope you liked it
and I will see you in the next lecture.