Amazon Rekognition is a service offered by Amazon Web Services that makes it easy to add powerful visual analysis capabilities to your applications. It is a deep learning powered, image & video recognition engine that detects faces, objects, and scenes. It also possesses intelligent capabilities such as extracting text, recognizing celebrities and identifying inappropriate content from images and videos.This leads us to some interesting propositions.

Amazon Rekognition uses a virtual container to store and index images. This is known as a collection. Collections can be created or deleted dynamically and they act as a repository of images for a single application. Images are loaded onto the collections from S3 buckets and then run through Amazon Rekognition engine.The results of a facial recognition match are always returned as a similarity score in percentage (%) between the input image with the stored reference images. This is similar to the probability score and is a good indication for deciding whether a given face input image matches one of the reference images or not. To verify the similarity score returned by Rekognition, we will test for three scenarios.Scenario 1 - Using an input image not belonging to any of the persons whose reference images are in the collection.Scenario 2 - Using an input image that belongs to the person whose reference images are in the collection.Scenario 3 - Using an input image that exactly matches with one of the person’s reference images in the collection.Follow the steps below to set up the test environment for Amazon Rekognition.

LUNA SDK supports multiple platforms and comes in two editions: the front-end edition and the complete edition. Frontend edition. The Frontend edition is intended for lightweight solutions that do not need to implement face descriptor extraction and matching functions.

The demo python program will run based on command line options. There are four options available for the users. Creating Collection (1) - Creation of a collection is the first step in operating the Rekognition service. Since all images will be indexed within a collection, the program allows user to create a collection. Deleting Collection (2) - Users can delete the existing collection and to erase all indexed data.

Index Face (3) - This operation registers a new face. The Rekognition service will accept a valid face image and return an Face Id. Match Face (4) - This is the actual facial recognition operation.

An input image is matched with all the indexed images and the program returns the similarity score with the best match.To perform a conclusive test of the facial recognition capabilities, an additional test image is provided for alice and mary which will be used as input. You can try scenario 3 by using alice-1.jpg as the image. Since this is the exact image which is already indexed, you will most likely get a 100% similarity score.For testing scenario 1 you can use scarlett-1.jpg which is a different person whose face is not indexed.

In this case you are likely to get no response.Amazon Rekognition supports a threshold value of similarity score for returning matched faces. This demo program uses a threshold of 80%. However, by using a lower threshold you can match faces with dissimilar persons as well.

This threshold needs to be tuned as per the application requirements.

Share this story.MOUNTAIN VIEW, CALIF.—Google is launching a new SDK for machine learning for its Firebase developer platform called 'ML Kit.' The new SDK offers ready-to-use APIs for some of the most common computer-vision use cases, allowing developers that aren't machine learning experts to still add some ML magic to their apps. This isn't just an Android SDK; it works on iOS apps, too.Typically, setting up a machine learning environment is a ton of work. You'd have to learn how to use a machine learning library like TensorFlow, acquire a ton of training data to teach your neural net to do something, and at the end of the day you need it to spit out a model that is light enough to run on a mobile device. ML Kit simplifies all of this by just making certain machine learning features an API call on Google's Firebase platform.

GoogleThe new APIs support text recognition, face detection, bar code scanning, image labeling, and landmark recognition. There are two versions of each API: a cloud-based version offers higher accuracy in exchange for using some data, and an on-device version works even if you don't have Internet. For photos, the local version of the API could identify a dog in a picture, while the more accurate cloud-based API could determine the specific dog breed. The local APIs are free, while the cloud-based APIs use the usual Firebase cloud API pricing.If developers do use the cloud-based APIs, none of the data stays on Google's cloud. As soon as the processing is done, the data is deleted.In the future, Google will add an API for Smart Reply. This machine learning feature is debuting in Google Inbox and will scan emails to generate several short replies to your messages, which you can send with a single tap. This feature will first launch in an early preview, and the computing will always be done locally on the device.

There's also a 'high density face contour' feature coming to the face detection API, which will be perfect for those augmented reality apps that stick virtual items on your face. ML Kit will also offer an option to decouple a machine learning model from an app and store the model in the cloud. Since these models can be 'tens of megabytes in size,' according to Google, offloading this to the cloud should make app installs a lot faster. The models first are downloaded at runtime, so they will work offline after the first run, and the app will download any future model updates.The huge size of some of these machine learning models is a problem, and Google is trying to fix it a second way with a future cloud-based machine learning compression scheme. Google's plan is to eventually take a full uploaded TensorFlow model and spit out a compressed TensorFlow Lite model with similar accuracy.This also works well with Firebase's other features, like Remote Config, which enables A/B testing of machine learning models across a user base.

Firebase can also switch or update models on the fly, without the need for an app update.Developers looking to try out ML Kit can find it in the.