Amazon Rekognition is one of the most sophisticated cloud-based image and video analysis platforms available today. It leverages advanced machine learning models trained on millions of images to identify objects, people, text, scenes, activities, and inappropriate content with high accuracy. With great power comes increased complexity – which begs the question, is Rekognition too complicated for the average user?
On the surface, getting started with Rekognition seems simple enough. You just upload your image or video file to the AWS Management Console or use one of the pre-built SDKs to integrate the service into your application.
From there, you can take advantage of highly accurate computer vision capabilities like:
* Face detection, analysis (gender, age range, emotions, etc.), recognition and celebrity recognition
* Object and scene detection across millions of objects, concepts, and scenarios
* Inappropriate/offensive content detection for explicit or suggestive imagery
* Text detection and optical character recognition (OCR)
* Video activity analysis like person pathing and specific activity detection
The potential use cases are endless. Major media companies use Rekognition to automatically categorize and catalog photo and video assets at massive scale. Developers build smart home security systems that recognize individuals and detect potentially suspicious activity. Dating apps analyze selfie uploads to filter explicit content and detect attributes like age and gender.
Other powerful applications include digitizing paperwork by scanning documents and extracting text, building mobile apps that parse business cards, automating video intelligence and content moderation for platforms like YouTube and Twitch, and enhancing user experiences for cameras and photo editing apps.
So, what’s the catch? While using Rekognition itself is relatively straightforward, getting the most value out of this service requires an investment of significant time and technical skills. More advanced capabilities like building custom label detection models, setting up automated data workflow integration, and managing critical data like face metadata necessitate skilled development resources.
For example, say you want to build a mobile app that captures business cards, extracts contact information using the Rekognition text detection API, and programmatically populates that data into a CRM system. This would involve setting up an automated AWS Lambda processing pipeline, building a frontend SDK integration, and likely using additional AWS services like S3 for image storage – certainly not a simple DIY task for the non-technical user.
The complexity is also reflected in Rekognition’s billing model, which can quickly add up costs for production usage at scale. Pricing has three main components:
1. Image/video data processed – The first 5,000 images uploaded per month are free. After that, you pay $0.001 per image for processing.
2. Facial analysis – You’re charged $0.0008736 per 1,000 facial analysis operations.
3. Storage retrieval – $0.0008 per 1,000 GET requests plus $0.023 per GB retrieved.
While the free tier is generous for basic testing, those costs can pile up fast if you’re processing millions of images and videos each month. For high volumes, you’d likely need to negotiate an enterprise pricing plan with AWS sales.
Conclusion
While Amazon Rekognition provides supremely powerful, accurate, and comprehensive computer vision capabilities powered by advanced deep learning models, it does come with a fairly steep learning curve and cost considerations that may make it unsuitable for small businesses or less technical users.
Non-technical teams or those with basic needs like scanning receipts or parsing basic image content may find cheaper, simpler “box solutions” like Google Cloud Vision AI or AWS’s own Rekognition Image (bundled image labeling service) more suitable.
But for heavy computer vision workloads requiring deep integrated analysis – like video intelligence, facial recognition at scale, or training custom models – Rekognition delivers powerhouse AI, if you have the resources and skills to wield it.