Amazon Rekognition

The traditional solutions to extract insights from images and videos are time-consuming and costly. These approaches require specialized skills, leaving room for low scalability and errors.

Amazon Rekognition, on the other hand, enables businesses to solve computer vision problems efficiently and without prior machine learning skills.

Core capabilities of Amazon Rekognition

Amazon Rekognition has more than a dozen powerful image and video analysis capabilities. Amazon Rekognition’s API can easily add these features to our application. Let’s discuss some of the key features here:

Image analysis

The image analysis technology of Amazon Rekognition is capable of providing multiple services. Some of them are as follows:

• Object detection: Amazon Rekognition can identify and categorize objects, scenes, segments, or even concepts from images.

• Content detection and filtering: Amazon Rekognition can filter inappropriate and unsafe text, content, and images. This allows for precise content moderation and enhanced safety.

• Facial analysis: Amazon Rekognition offers powerful facial analysis services. It can not only detect and analyze faces, but can compare different faces in images and videos. It is also powerful enough to analyze emotions, gender, and age. This can help solve problems such as user verification and categorizing people according to their age or gender.

Video analysis

The video analysis technology of Amazon Rekognition is capable of providing multiple services. Most of these services are similar to image analysis such as object, text, and unsafe content detection and facial analysis. The specific video analysis capabilities of Amazon Rekognition are as follows:

• Identification in motion: Amazon Rekognition can do “People pathing,” a capability of tracking the path of the identified people as they move across consecutive frames in videos. This capability allows Amazon Rekognition to create a trajectory of specific people in videos. It can also provide insights such as the person's location during tracking and facial landmarks in videos.

• Video segmentation: Amazon Rekognition can identify useful and unimportant segments of videos. It can pinpoint the portions of videos that are not relevant to the topic of the content. This capability is useful for use cases such as video editing and indexing.

• Face liveness: Face liveness detection is a security measure used to authenticate users to ensure the right individuals are using the resources. Amazon Rekognition detects face liveness through a short selfie video, reading various indicators such as eye blinks, facial movements, etc. It can also detect movement patterns, eliminating the possibility of using pre-recorded videos.

How Amazon Rekognition work

Amazon Rekognition allows its users to upload images or videos and choose analysis features such as facial analysis, content detection, and others. The analysis typically takes a few seconds. Once the analysis is complete, users can view the results within the console.

The service also allows its users to interact with the results and download them for later use. All the image and video analysis capabilities of Amazon Rekognition can also be integrated into applications. For application integration, Amazon Rekognition offers two sets of APIs - Amazon Rekognition Image API for image analysis and Amazon Rekognition Video API for video analysis. These APIs are pretrained by Amazon but can be customized according to the requirements.

Use case examples

Amazon Rekognition can be used in various scenarios and can be an ideal solution for challenges like content moderation, user authentication, media analysis, etc.

Content moderation

Consider an online social media platform where users can upload images and videos. The platform aims to maintain a safe and healthy environment by filtering offensive content, images, and videos, ensuring a positive user experience.

Amazon Rekognition would be an excellent solution in such a scenario. With the content moderation features of Amazon Rekognition, the social media platform can easily filter offensive and unsafe content. Amazon Rekognition’s image and video APIs can be integrated into the social media application without any model training or building extensive infrastructure. Amazon Rekognition allows us to set a minimum confidence threshold for flagging items. Once flagged, we can optionally manually review the flagged items using the Amazon Augmented AI feature.

Online examination

Consider another use case where an educational institute wants to conduct an online examination. The institute uses Amazon Rekognition to ensure a fair examination process. Before the exam, students must authenticate themselves using Amazon Rekognition’s face-liveness feature.

After the exam, Amazon Rekognition’s facial analysis capabilities are used to review the recorded video footage and identify any instances of suspicious behavior or cheating.