Amazon’s computer vision service, AWS Rekognition, helps embed visual analysis to various applications. It can search, verify, and organize tens of thousands of images and analyze motion-based content. The service offering is developed on a highly scalable deep learning technology, which allows the detection of objects, faces, and scenes, reads textual content and identifies any inappropriate content. Moreover, it can also perform face comparison.

Rekognition Image detects any objects and scenes in images, whereas Rekognition Video detects the movement of objects in a frame. It is capable of detecting human activity even when the face is not visible. Therefore, it makes up a use case in receiving notification if a delivery person comes near your entry gate for delivery. A few other widely implemented use cases for Rekognition Image include face recognition, sentimental analysis, and those for Rekognition Video are search indexes and explicit content filtering in online media.

The best part about this service is that you need not have the expertise to build, maintain or upgrade pipelines. To attain accuracy in any computer vision task, a considerable amount of labelled ground truth data and Graphics Processing Units (GPU) should be used for training to allow high computations. However, all this is automatically taken care of while using Rekognition. It is pre-trained for recognition related tasks, hence, leading to a fully managed deep learning pipeline. This helps in keeping your focus on the design and development of the core application.

The following figure shows the functions supported by the Rekognition service.

• Object and Scene Detection: With AWS Rekognition, thousands of object models are pre-trained for recognition.
• Custom Labeling: With custom labelling, you may identify the objects and scenes in images specific to your business needs.
• Content Moderation: It allows detection of explicit content having violence, weapons, and any disturbing or offensive content. It is primarily used in child apps, dating sites, photo sharing apps etc.
• Facial Analysis: AWS Rekognition enables the detection of faces in images and videos. It also allows the analysis of facial attributes detected in the image.
• Face Comparison: It allows you to compare two faces and know the similarity levels.
• Facial Recognition:  It enables identifying any individual by matching the captured frame from a collection or group of face frames.
• Celebrity Recognition: This function helps in recognizing famous personalities in the field of sports, politics, entertainment, business etc.
• Text Detection: It helps recognize the text in any image/video, such as addresses, captions, and subtitles. Visual search on social media and vehicle plate number identification by traffic cameras are its significant use cases.
• PPE Detection: This is an exciting feature that detects PPE worn by the people in an image. This function helps improve workplace safety practices.
• People Pathing: AWS Rekognition can track the path people take in videos, thus providing the location and facial landmarks. It helps provide customer insights concerning their movement patterns in a shopping mall or a grocery store.
• Black Frames Detection: Usually, any video with advertisements will have black/empty frames used as cues for ads to begin. Using AWS Rekognition, this media analysis is automated.
• End Credits Detection: Rekognition helps identify the beginning point for the end credits in a video so that prompts like “next movie” can be timely shown.
• Shot Detection: A series of interrelated continuous images is called a shot. AWS Rekognition helps detect the start, end and duration of every shot, which helps set up thumbnails during video transition and identify spots for ads to begin.
• Color Bars Detection: AWS Rekognition helps detect sections in a video comprising SMPTE bars that ensure the color is accurately calibrated. It is usually helpful in the detection of broadcast signal loss.

Fig. Comparison of top computer vision APIs by public cloud providers