Facial Recognition Datasets for Machine Learning

Bias in machine learning is one of the major threats to effective Computer Vision. Mitigate this bias by utilizing globally sourced datasets. Our crowd is the answer for precise annotations and inclusive datasets.

Facial Recognition

What is Facial Recognition?

Facial recognition is an application of Computer Vision. It represents a machine’s ability to first detect a face, and then to recognize that face as belonging to a specific person. Some of its major applications include biometric scanning and security. There are other more enjoyable applications of facial recognition, such as improving shopping experiences. But there is no denying that this form of technology is not free of controversy.

Why MarsCrowd?

A Crowd of

Trained Specialists

A Crowd of


A Crowd of

In-House Linguists

Facial Recognition in Action

When we work together on a project, you can expect a seamless process for delivery. Facial recognition projects can be broken down into 5 general steps from the collection of image data to the continual improvement of the model while repeating the process. The overall goal is to accurately label a diverse dataset of images containing to improve your AI model. The process looks something like the model below:


Training of

classification model

Text Data Collection for Machine Learning Process

Text data collection

Design of

feature extractors

Performance evaluation & visualization

How Does Facial Recognition Work?

On our in-house platform, our Crowd utilizes landmark annotation tools to manual label your image datasets. We assign a project manager and resource manager to your project and run QA checks regularly before handing the data back to you. We work side-by-side to provide sufficiently accurate training data for your facial recognition models.
The diversity of your datasets is an important component. This will enable your model to work across multiple contexts. We can not only label the data, we can help you collect it with our global Crowd spanning 50+ countries. Get the data you need labeled to the level you deserve.

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