Remote Freelance | Face Deduplication Collection

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 The project compensation rate is $0.55 USD per accepted image. 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.

Collect and submit a diverse dataset of current neutral selfies, head-pose captures, and historical facial images. These images will be used to support machine-learning research and facial recognition model training.

Participants must be able to provide a minimum of 20 valid images and use a Gmail address for application. No specific education is required to perform the tasks.

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Data Collection
• Photography

• Data & Analytics
• Technology
• Science & Research
• Administrative
• Consulting

Expérience minimale

0+ ans

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Entry level