How Amazon Rekognition Works

1. Introduction

The commercial uses of computer vision algorithms have exploded in the last two years, whether it’s for self-driving cars, national security, or healthcare. However, its application necessitates a thorough understanding, which Amazon Rekognition services may be able to replace.

2. What Is Amazon Rekognition?

Amazon Rekognition is a Computer Vision service that is fully managed by AWS and uses highly scalable deep learning technology to analyze images and video. With Amazon Rekognition, various business challenges can be solved without requiring you to have an expert in machine learning.

Amazon Rekognition is a simple API that instantly analyses any image or video file. This service is trained to accurately identify objects, people, scenes, activities, text, and inappropriate content. Simply upload a photo or video to the Amazon Rekognition API, and the service will recognize objects, text, people, activities, and scenes. It can detect any inappropriate content; you can enforce policies and comply with regulations with inappropriate content.

3. Type of Analysis

The Amazon Rekognition Image and Video API may do the following types of analysis.

• Inappropriate or offensive content in images and videos: Amazon Rekognition can detect obscene and violent content. Use DetectModerationLabels to detect potentially dangerous images. StartContentModeration can be used to detect hazardous recorded videos.
• Text detection: Amazon Rekognition can recognize and transform text in images into machine-readable text. Use DetectText to detect text in photos.
• Celebrities: Thousands of celebrities can be recognized in images and videos saved on Amazon Rekognition.
• PPE stands for personal protective equipment in an image; Amazon Rekognition can locate PPE (Personal Protective Equipment) worn by humans. For example, face covers, hand covers, and headcovers are all detectable by Amazon Rekognition. Amazon Rekognition checks whether a piece of personal protective equipment (PPE) covers the proper body part. There are also bounding boxes and PPE equipment available for people who have been detected. To detect PPE in images, use DetectProtectiveEquipment.
• People paths: People spotted in a saved video can be tracked with Amazon Rekognition.
• Faces & Face search: Faces can be recognized in images and videos created by Amazon Rekognition. You may collect information on where faces are seen in an image or video, facial landmarks like eye position, and detect emotions like happy or sad by using facial landmarks like eye position and identifying emotions like happy or sad. Amazon’s Rekognition. To detect faces in photos, use DetectFaces. StartFaceDetection can be used to recognize faces in saved videos.
• Amazon Rekognition allows for Facial Recognition. Facial data is indexed into a collection, which serves as a storage container. Faces recognized in photos, archival videos, and live videos can then be linked to face data in the collection. To find known faces in photos, use DetectFaces. StartFaceDetection can be used to look for known faces in videos that have been saved. Finally, CreateStreamProcessor can be used to search live video streams for known people.
• Labels & Custom labels: A label can be referred to any of the objects, events, concepts, or activities. Labels in videos and images can be detected using Amazon Rekognition. Use DetectLabels to find labels in images. Use StartLabelDetection to find labels in stored videos.

Amazon Rekognition Custom Labels may identify goods and scenes in images specific to your company’s needs.

4. Why Amazon Rekognition?

Some of the benefits of using Amazon Rekognition are as follows:

• Deep learning-based image and video analysis can be integrated with apps.
• Amazon Rekognition is a Scalable service that enables you to analyze millions of images.
• There are no minimum fees or upfront commitments for using You pay for the images and videos you analyze and the face metadata you store.
• Easy Integration with other AWS services provides additional benefits on it. You can call the Amazon Rekognition API directly from Lambda in response to Amazon S3 events

5. Common Applications of Amazon Rekognition

Few Amazon Rekognition’s use cases are mentioned below:

6. General Architecture Using Amazon Rekognition’s API

Metadata such as camera position, time, and other camera data can be uploaded to S3 along with the frames. As images are processed, you may either delete them right away or use an S3 bucket’s lifecycle policy to set them to expire after a certain amount of time, as required by your organization’s data retention policy. Amazon Lambda can trigger Amazon Rekognition with S3 events & also generate notifications using AWS SNS.

7. Conclusion

AWS Rekognition is a simple, easy, rapid, and cost-effective way to implement computer vision models without having to worry about knowledge of computer vision’s core functioning. All you need to know is how to use the API provided by the client libraries.

8. About CloudThat

As a pioneer in the Cloud consulting realm, CloudThat is AWS (Amazon Web Services) Advanced Consulting Partner, AWS authorized Training Partner, Microsoft Gold Partner, and Winner of the Microsoft Asia Superstar Campaign for India: 2021. Our team has designed and delivered various Disaster Recovery strategies to our customers. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere to advance in their businesses.

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9. FAQs

1. What is Deep Learning?
   Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
2. What is the accuracy of Amazon Rekognition?
   Rekognition is now up to 80% more accurate in distinguishing between people who look very similar to each other, and up to 35% more accurate in recognizing the same person with substantial changes in their appearance.

WRITTEN BY Shubham Dubey

Shubham Dubey works as a Sr. Research Associate at CloudThat. He has 3+ years of experience in AI/ML. He is highly passionate about learning new skills and technologies.