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 surrounding s.
- Current Head-Pose Captures – selfies captured in assigned head directions to introduce pose variati on.




- Historical Images – older photos from participants’ galleries to capture natural aging and long-term appearance chan ges.

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 r eview

The project compensation rate is $0.55 USD per accepted i mage. Note: Please use a Gmail address as your primary account when applying for this p roject.

Qualificat

ion path No specific education is needed to perform the proje ct task.

📌 TFH - Face Deduplication Collection (Buenos Aires)
🏢 TELUS Digital AI Data Solutions
📍 Buenos Aires