Overview

Rekognition API

Stacks5

Followers25

Votes0

OpenFace

Stacks31

Followers104

Votes3

GitHub Stars15.4K

Forks3.6K

OpenFace vs Rekognition API: What are the differences?

Introduction:

OpenFace and Rekognition API are two popular tools used for facial recognition in various applications. Here are key differences between the two:

Algorithm Complexity: OpenFace uses a deep neural network for facial recognition, which allows for more detailed analysis and matching of facial features. On the other hand, Rekognition API uses machine learning algorithms to quickly identify and match faces, making it more suitable for real-time applications.
Cost: OpenFace is an open-source project, making it free to use for both personal and commercial purposes. In contrast, Rekognition API is a paid service provided by Amazon Web Services, with costs varying based on usage and volume of requests.
Customization: OpenFace offers more flexibility and customization options, allowing developers to fine-tune the facial recognition algorithms to suit their specific needs. Rekognition API, on the other hand, provides a more standardized and user-friendly interface, making it easier to implement but less adaptable to unique requirements.
Accuracy: OpenFace has been lauded for its high accuracy in facial recognition tasks, particularly in challenging conditions such as poor lighting or occlusions. Rekognition API, while still accurate, may not perform as well in these scenarios due to its reliance on predefined algorithms.
Integration: OpenFace can be easily integrated into existing projects and systems, thanks to its open-source nature and compatibility with multiple programming languages. Rekognition API, being a cloud-based service, requires internet connectivity and API calls for integration, which may pose challenges in offline or restricted environments.
Storage and Data Privacy: OpenFace allows for local storage and processing of facial data, ensuring greater control over data privacy and security. In comparison, Rekognition API stores and processes data in the cloud, raising concerns about data privacy and compliance with regulations.

In Summary, OpenFace and Rekognition API differ in terms of algorithm complexity, cost, customization, accuracy, integration, and data privacy, catering to different needs and preferences in facial recognition technology.

Detailed Comparison

Rekognition API	OpenFace
ReKognition API offers services for detecting, recognizing, tagging and searching faces and concepts as well as categorizing scenes in any photo, through a RESTFUL API. We process and analyze photos from anywhere, so you can mix and match photo sources with user IDs, which can enable you to, say, recognize objects in Facebook and Flickr photos.	OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.
Detect and recognize faces; Detect face with age, race, glasses, gender, mouth, eye information. Recognize objects, scenes, landmarks and more	Detect faces with pre-trained models; Transform faces for the neural network; Use deep neural networks to reprsent or embed the face on a hypersphere; Apply favorite clustering or classification techniques to the features to complete recognition task
Statistics
GitHub Stars -	GitHub Stars 15.4K
GitHub Forks -	GitHub Forks 3.6K
Stacks 5	Stacks 31
Followers 25	Followers 104
Votes 0	Votes 3
Pros & Cons
No community feedback yet	Pros 3 Open Source

What are some alternatives to Rekognition API, OpenFace?

Kairos API

Commercial-grade emotion analysis, face detection and recognition engine provided as a public API. Kairos takes the complexity out of facial recognition and emotion analysis so you can focus on building a great product.

FaceChain

It is a deep-learning toolchain for generating your Digital-Twin. With a minimum of 1 portrait-photo, you can create a Digital-Twin of your own and start generating personal portraits in different settings.