Amazon Rekognition

Amazon Rekognition is a managed computer-vision service for images and video. It provides pre-trained APIs for object and scene detection, facial analysis, text-in-image OCR, content moderation, celebrity recognition, and PPE detection — plus Custom Labels for training bespoke image classifiers with a small labeled dataset.

Key Features:

• Label Detection: Identifies thousands of objects, scenes, and activities in images or video frames with confidence scores.
• Face Analysis & Comparison: Extracts facial attributes (emotion, age range, eyes open, pose) and compares faces across images; face collections support 1-to-many search.
• Content Moderation: Flags explicit, suggestive, violent, or otherwise unsafe content with a hierarchical taxonomy — useful for UGC platforms.
• Text in Image (OCR): Detects and reads text within photos (signs, license plates, product packaging) — distinct from Textract's document focus.
• Custom Labels: Train a domain-specific image classifier or object detector with as few as ~10 images per label.
• Video Analysis: Asynchronous jobs over S3-hosted video detect labels, faces, persons, celebrities, and moderation events along a timeline.
• Streaming Video: Real-time face detection on Kinesis Video Streams for live security and engagement use cases.

Common Use Cases:

• Media Asset Tagging: Auto-tag large image/video libraries with labels, scenes, and celebrities for search.
• Content Moderation: Gate user-generated uploads before publish to reduce policy violations.
• Identity Verification: Compare a selfie to a government-ID photo for onboarding or access control (paired with liveness detection).
• Retail & Inventory: Detect products on shelves or count items in warehouse photos with Custom Labels.
• Workplace Safety: Detect PPE compliance (hardhats, masks, gloves) in site photos.

Example: Detect Labels in an Image

import boto3

rek = boto3.client("rekognition", region_name="us-west-2")

resp = rek.detect_labels(
    Image={"S3Object": {"Bucket": "my-images", "Name": "warehouse/row-4.jpg"}},
    MaxLabels=10,
    MinConfidence=75,
)

for label in resp["Labels"]:
    print(f"{label['Name']:20s} {label['Confidence']:.1f}%")
  

Rekognition covers the common vision tasks end-to-end; reach for SageMaker when you need fully custom models, non-standard outputs, or on-premises deployment.