Introduction to Amazon Rekognition and its use cases

In this recipe, we will learn about Amazon Rekognition. We will also learn about the use cases of Amazon Rekognition.

Recipe Objective - Introduction to Amazon Rekognition and its use cases?

The Amazon Rekognition is widely used and is defined as the computer vision service which was introduced by Amazon in 2016 year. Amazon Rekognition automates users' image and video analysis with machine learning capabilities. The computer vision service which is provided by Amazon Rekognition is divided into two categories: which can be divided into two categories i.e.firstly, the algorithms that are pre-trained on data collected by Amazon or its partners and the algorithms that a user can train on the custom dataset. Amazon Rekognition enables the quick addition of the pre-trained or the customizable computer vision APIs to user's applications without the need of building machine learning (ML) models and the infrastructure from scratch. Amazon Rekognition also scales up and down based on user's business needs with the fully managed Artificial Intelligence capabilities and users can pay only for the images and videos they analyze. Amazon Rekognition enables users to analyze millions of images and videos within minutes and further augment human visual review tasks with artificial intelligence (AI) capabilities. Amazon Rekogniton provides amazon rekognition Activities Package brings the power of the Amazon Rekognition image service to the UiPath Studio so, using the Amazon Rekognition Image APIs, the activities package provides users with the image analysis capabilities like Detect, identify, and compare faces & Identify and return distinct objects (Eg. label detection) & Detect unsafe content & Identify and return text & Create collections for storing faces.

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Benefits of Amazon Rekognition

  • The Amazon Rekognition helps in detecting text in images and videos. Amazon Rekognition can further convert the detected text into machine-readable text and users can use the machine-readable text detection in images to implement solutions like Visual search that is retrieving & displaying images that contain the same text. Users can also implement Content insights that are providing insights into themes that occur in the text that's recognized in extracted video frames and the user's application can search recognized text for the relevant content, such as the news, sports scores, athlete numbers, and captions. Further, users can implement Navigation which is developing a speech-enabled mobile app for visually impaired people which recognizes names of the restaurants, shops, or street signs. Following Public safety and transportation support that is detecting car license plate numbers from the traffic camera images and further Filtering that is filtering the personally identifiable information (PII) from various images. Amazon Rekognition automates the Personal Protective Equipment (PPE) detection to improve workplace safety practices and thus provide workplace safety. Amazon Rekognition augments manual checks with the automated PPE detection and Analyze images from cameras across all user's on-premises sites to detect if employees and customers are wearing PPE were required and thus automating PPE detection at scale. Further, It alerts or notifies the employees and customers about missing PPE in time to prevent lapses and improve everyone’s safety. and helps in maintaining the PPE detection records to comply with the occupational safety regulations and further reduce the risk of penalties or fines so, it helps in reducing the financial and human risk. The Amazon Rekognition provides users with the option to pay for the images and videos that they analyze, and the face metadata that they store and there are no minimum fees or upfront commitments. So, Users can get started for free, and save more as they grow with the Amazon Rekognition tiered pricing model and thus providing Low-cost service.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Rekognition and Use cases of Amazon Rekognition.

Use cases of Amazon Rekognition

    • It provides Unsafe content detection

Amazon Rekognition can help in detecting the adult and violent content in the images and in the stored videos. Developers can use the returned metadata to filter the inappropriate content based on their business needs. Also, Beyond flagging an image based on the presence of the unsafe content, the API also returns the hierarchical list of labels with the confidence scores. So, these labels indicate the specific categories of the unsafe content, which further enables granular filtering and management of large volumes of user-generated content (UGC). For example, social and dating sites, photo-sharing platforms, blogs & forums, Apps for children, Ecommerce websites.

    • It provides Celebrity recognition

Amazon Rekognition helps in recognizing the celebrities within the supplied images and in the videos. Amazon Rekognition can recognize thousands of celebrities across several categories such as the politics, sports, business, entertainment & media.

    • It provides Custom labels

Using Amazon Rekognition Custom Labels, users can identify the objects and scenes in the images that are specific to the user's business needs. For example, users can find their logo in the social media posts, identify their products on the store shelves, classify machine parts in an assembly line, distinguish healthy & infected plants, or further detect the animated characters in videos.

    • It provides Facial Search

Using Amazon Rekognition, users can search images, store videos, and stream videos for the faces that match those stored in the container known as the face collection. A face collection is defined as an index of faces that users own and manage. So, Searching for people based on their faces requires two major steps in the Amazon Rekognition, firstly Indexing of the faces and secondly Searching for the faces.

    • It provides Face-based user verification

Amazon Rekognition enables users to build applications to confirm user identities by further comparing their live image with the reference image.

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