AMAZON REKOGNTION

We are living in an age where machines can identify people, places, objects and much more- in images and videos, with high accuracy and high efficiency. A prime example of this is Amazon Rekognition - a service that can classify and understand useful information from the images (including videos).

In this blog, we have a look at Amazon Rekognition - understanding what it does, its features and how your firm can leverage this service.

So, what is Amazon Rekognition?

It is a powerful deep-learning-based video and image analyzer. It is a service that makes it easy to add image and video analysis to applications and other platforms by using deep learning technology that requires no mastering in machine learning on the developer's part. It can identify the objects and scenes in images that are exact to your business needs. It can be easily integrated with other services offered by AWS such as AWS S3 or AWS Lambda, with a pay as you go model applied to this service.

Deep learning, Amazon Rekognition's foundation, is a sub-set of Machine Learning and is also constituted under Artificial Intelligence. It arrives at high level conclusions from huge amounts of unprocessed data by using graphs and multiple other processing layers to increase accuracy- composed of both linear and non-linear transformations. Deep learning in the AI and ML space is also one that is often linked to imitating how the human brain functions.

Types of Analysis

Amazon Rekognition provides two types of APIs (Application Programming Interface) bifurcated by Image and Video, which will be further elaborated on later in the blog. Now, we have a look at the types of analysis that the Amazon Rekognition Image API and Amazon Rekognition Video API can perform.

Labels: A label refers to objects (for example- chairs, grass, or table), events (for example, a wedding, graduation, or birthday party), concepts (for example, a landscape, evening, and nature) or activities (for example, skateboarding). Amazon Rekognition can detect labels in images and videos. However, activities are not detected in images.

Custom labels: Amazon Rekognition Custom Labels can identify the objects and scenes in images that are specific to your business needs by training a machine learning model. For example, you can train a model to detect logos or detect engineering machine parts on an assembly line. Amazon Rekognition provides a console that you use to create, train, evaluate, and run a machine learning model.

Faces: With Amazon Rekognition, you can get information about where faces are detected in an image or video, facial landmarks such as the position of eyes, and detected emotions such as happy or sad. You can also compare a face in an image with faces detected in another image. Information about faces can also be stored for later retrieval.

Face search:?Amazon Rekognition can search for faces. Facial information is indexed into a container known as a collection. Face information in the collection can then be matched with faces detected in images, stored videos, and streaming video.

People paths: Amazon Rekognition can track the paths of people detected in a stored video. Amazon Rekognition Video provides path tracking, face details, and in-frame location information for people detected in a video.

Personal Protective Equipment: Amazon Rekognition detects face covers, hand covers, and head covers, ie Personal Protective Equipment (PPE). Amazon Rekognition predicts if an item of PPE covers the appropriate body part. You can also get bounding boxes for detected persons and PPE items.

Celebrities: Amazon Rekognition can recognize thousands of celebrities in images and stored videos. You can get information about where a celebrity's face is located on an image, facial landmarks, and the pose of a celebrity's face. You can also get further information about a recognized celebrity, like the emotion expressed, and presentation of gender.

Text detection: Amazon Rekognition Text in Image can detect text in images and convert it into machine-readable text.

Inappropriate or offensive content: Amazon Rekognition can analyse images and stored videos for adult and violent content. Multiple Social Media platforms are dependent on this service for the same.

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Further Understanding Amazon Rekognition

Amazon Rekognition includes a simple, easy-to-use API that can quickly analyse any image or video file. Amazon Rekognition is always learning from new data, while continually adding new labels and facial comparison features to the service.

(what is an Application Programming Interface (API) ? An application programming interface is a way for two or more computer programs to communicate with each other. It is a type of software interface, offering a service and enabling functions to other pieces of software)

Amazon Rekognition provides two API sets: Amazon Rekognition Image for analysing images; and Amazon Rekognition Video for analysing videos. Both APIs analyse images and videos to provide insights you can use in your applications.

For example, you could use Amazon Rekognition Image to enhance the customer experience for a photograph management application by identifying photos with certain objects or certain facial features like smiles- making it a much more satisfying customer experience.

Amazon Rekognition is further grouped into two categories-

Non-storage API operations: Here, Amazon Rekognition doesn't restore any information. You provide input images and videos, the operation performs the analysis, and returns results, but nothing is saved by Amazon Rekognition.

Storage-based API operations: Amazon Rekognition servers can store detected facial information in containers known as collections. Amazon Rekognition provides additional API operations you can use to search the persisted face information for face matches.

Use Cases and Examples of Rekognition

Benefits:

1. Integrating powerful image and video analysis into your apps
2. Deep learning-based image and video analysis
3. Scalable image analysis
4. Integration with other AWS services
5. Low cost, pay as you go system

Examples:

National Football League (NFL): By using Amazon Rekognition- Custom Labels, they can automatically generate metadata tags tailored to specific use cases for their business and provide searchable facets for their content creation teams. These tools allow their production teams to leverage this data directly and provide enhanced products to their customers across all of their media platforms.

Aella Credit: This financial services company provides easy access to credit in emerging markets using biometric, employer, and mobile phone data. They have used Amazon Rekognition to solve major challenges with regard to identity verification and validation- to enter emerging markets in this space.

Scripps Networks Interactive: They are a leading developer of engaging lifestyle content in the home, food and travel categories for television, the internet and emerging platforms. Amazon Rekognition enables them to quickly and efficiently add value through various automated metadata tagging processes, and images and video segments are much easier to find for their enterprise and customers. This enables increased cycle times, productivity, and efficiency, and adds revenue opportunities that benefit their bottom line.

How can Ataloud help?

Connect with an Ataloud consultant today ([email protected]) for a seamless experience for your business' transition to the cloud. We can analyze, discuss and help validate your AWS billing and usage patterns, perform routine audits, perform log analysis, analyse and monitor performances- on top of the other managed services that we offer.?