Face Deduplication Collection
Role Overview
This role involves collecting facial images (neutral selfies, head-pose captures, and historical photos) for machine-learning research and facial recognition model training. Participants work independently, following guidelines to capture diverse images, and must submit a minimum of 20 valid images to earn payment. The work is remote and flexible, with no specific education required.
Perks & Benefits
This is a remote, freelance opportunity with flexible hours, allowing you to work from anywhere. You can earn up to $0.55 per accepted image, with a maximum payout based on 24 images. The project offers the chance to contribute to cutting-edge AI research and development.
Full Job Description
The project compensation rate is $0.55 USD per accepted image. The objective of this project is to collect a large and diverse dataset of current neutral selfies, head-pose captures, and historical facial images to support machine-learning research and facial recognition model training at TELUS.
The focus is on capturing real-world variation across lighting, poses, expressions, accessories, environments, and aging to improve model accuracy and robustness. The collection includes:
" Current Neutral Selfies clean frontal selfies serving as high-quality identity references, with natural variation in appearance and surroundings. " Current Head-Pose Captures selfies captured in assigned head directions to introduce pose variation. " Historical Images older photos from participants galleries to capture natural aging and long-term appearance changes.
To qualify for payment, you must submit a minimum of 20 valid images. The maximum payout is based on 24 accepted images. Due to the strict automated and manual Quality Control (QC) process, we strongly recommend submitting 30 images to help ensure that enough images remain valid after review
Note: Please use a Gmail address as your primary account when applying for this project.
Qualification path
No specific education is needed to perform the project task.
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