Amazon Rekognition | General Flashcards
What is Amazon Rekognition?
General
Amazon Rekognition | Machine Learning
Amazon Rekognition is a service that makes it easy to add powerful visual analysis to your applications. Rekognition Image lets you easily build powerful applications to search, verify, and organize millions of images. Rekognition Video lets you extract motion-based context from stored or live stream videos and helps you analyze them.
Rekognition Image is an image recognition service that detects objects, scenes, and faces; extracts text; recognizes celebrities; and identifies inappropriate content in images. It also allows you to search and compare faces. Rekognition Image is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images daily for Prime Photos.
Rekognition Image uses deep neural network models to detect and label thousands of objects and scenes in your images, and we are continually adding new labels and facial recognition features to the service. With Rekognition Image, you only pay for the images you analyze and the face metadata you store.
Rekognition Video is a video recognition service that tracks people; detects activities; and recognizes objects, celebrities, and inappropriate content in videos stored in Amazon S3 and live video streams from Acuity. Rekognition Video detects persons and tracks them through the video even when their faces are not visible, or as the whole person might go in and out of the scene. This makes investigation and real-time monitoring of individuals like Persons of Interest easy and accurate. For example, this could be used in an application that sends a real-time notification when someone delivers a package to your door. Rekognition Video allows you also to index metadata like objects, activities, scene, celebrities, and faces that make video search easy.
What is deep learning?
General
Amazon Rekognition | Machine Learning
Deep learning is a sub-field of Machine Learning and a significant branch of Artificial Intelligence. It aims to infer high-level abstractions from raw data by using a deep graph with multiple processing layers composed of multiple linear and non-linear transformations. Deep learning is loosely based on models of information processing and communication in the brain. Deep learning replaces handcrafted features with ones learned from very large amounts of annotated data. Learning occurs by iteratively estimating hundreds of thousands of parameters in the deep graph with efficient algorithms.
Several deep learning architectures such as convolutional deep neural networks (CNNs), and recurrent neural networks have been applied to computer vision, speech recognition, natural language processing, and audio recognition to produce state-of-the-art results on various tasks.
Amazon Rekognition is a part of the Amazon AI family of services. Amazon AI services use deep learning to understand images, turn text into lifelike speech, and build intuitive conversational text and speech interfaces.
Do I need any deep learning expertise to use Amazon Rekognition?
General
Amazon Rekognition | Machine Learning
No. With Amazon Rekognition, you don’t have to build, maintain or upgrade deep learning pipelines.
To achieve accurate results on complex computer vision tasks such as object and scene detection, face analysis, and face recognition, deep learning systems need to be tuned properly and trained with massive amounts of labeled ground truth data. Sourcing, cleaning, and labeling data accurately is a time-consuming and expensive task. Moreover, training a deep neural network is computationally expensive and often requires custom hardware built using Graphics Processing Units (GPU).
Amazon Rekognition is fully managed and comes pre-trained for image and video recognition tasks, so that you don’t have invest your time and resources on creating a deep learning pipeline. Amazon Rekognition continues to improve the accuracy of its models by building upon the latest research and sourcing new training data. This allows you to focus on high-value application design and development.
What are the most common use cases for Amazon Rekognition?
General
Amazon Rekognition | Machine Learning
The most common use-cases for Rekognition Image include:
Searchable Image Library
Face-Based User Verification
Sentiment Analysis
Facial Recognition
Image Moderation
License Plate Recognition
The most common use-cases for Rekognition Video include:
Immediate response for public safety and security
Investigative analysis of events for public safety
Search Index for video archives
Easy filtering of video for explicit and suggestive content
How do I get started with Amazon Rekognition?
General
Amazon Rekognition | Machine Learning
If you are not already signed up for Amazon Rekognition, you can click the “Try Amazon Rekognition” button on the Amazon Rekognition page and complete the sign-up process. You must have an Amazon Web Services account; if you do not already have one, you will be prompted to create one during the sign-up process. Once you are signed up, try out Amazon Rekognition with your own images and videos using the Amazon Rekognition Management Console or download the Amazon Rekognition SDKs to start creating your own applications. Please refer to our step-by-step Getting Started Guide for more information.
What APIs does Amazon Rekognition offer?
General
Amazon Rekognition | Machine Learning
Amazon Rekognition Image offers APIs to detect objects and scenes, detect and analyze faces, recognize celebrities, detect inappropriate content, and search for similar faces in a collection of faces, along with APIs to manage resources. Rekognition Image also offers APIs to compare faces and extract text, while Rekognition Video also offers APIs to track persons and manage live stream video from Acuity. For details, please refer to the Amazon Rekognition API Reference.
What image and video formats does Amazon Rekognition support?
General
Amazon Rekognition | Machine Learning
Amazon Rekognition Image currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array. Amazon Rekognition Video operations can analyze videos stored in Amazon S3 buckets. The video must be encoded using the H.264 codec. The supported file formats are MPEG-4 and MOV. A codec is software or hardware that compresses data for faster delivery and decompresses received data into its original form. The H.264 codec is commonly used for the recording, compression and distribution of video content. A video file format may contain one or more codecs. If your MOV or MPEG-4 format video file does not work with Rekognition Video, check that the codec used to encode the video is H.264.
What file sizes can I use with Amazon Rekognition?
General
Amazon Rekognition | Machine Learning
Amazon Rekognition Image supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array. Amazon Rekognition Video supports up to 8 GB files and up to 2 hour videos when passed through as an S3 file.
How does image resolution affect the quality of Rekognition Image API results ?
General
Amazon Rekognition | Machine Learning
Amazon Rekognition works across a wide range of image resolutions. For best results we recommend using VGA (640x480) resolution or higher. Going below QVGA (320x240) may increase the chances of missing faces, objects, or inappropriate content; although Amazon Rekognition accepts images that are at least 80 pixels in both dimensions.
How small can an object be for Amazon Rekognition Image to detect and analyze it?
General
Amazon Rekognition | Machine Learning
As a rule of thumb, please ensure that the smallest object or face present in the image is at least 5% of the size (in pixels) of the shorter image dimension. For example, if you are working with a 1600x900 image, the smallest face or object should be at least 45 pixels in either dimension.
How does video resolution affect the quality of Rekognition Video API results?
General
Amazon Rekognition | Machine Learning
The system is trained to recognize faces larger than 32 pixels (on the shortest dimension), which translate into a minimum size for a face to be recognized that varies from approximately 1/7 of the screen smaller dimension at QVGA resolution to 1/30 at HD 1080p resolution. For example, at VGA resolution, users should expect lower performances for faces smaller than 1/10 of the screen smaller dimension.
What else can affect the quality of the Rekognition Video APIs ?
General
Amazon Rekognition | Machine Learning
Besides video resolution, heavy blur, fast moving persons, lighting conditions, pose may affect the quality of the APIs.
What is the preferred user video content that is suitable for Rekognition Video APIs?
General
Amazon Rekognition | Machine Learning
This API works best with consumer and professional videos taken with frontal field of view in normal color and lighting conditions. This API is not tested for black and white, IR or extreme lighting condition. Applications that are sensitive to false alarms are advised to discard outputs with confidence score below a selected (application-specific) confidence score.
