Face Deduplication Collection
- Canada
Part-time
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Job details
- Job type
- Part-time
- Posting language
- English
- Posted
Original job description
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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
Qualification path
No specific education is needed to perform the project task.
More about this job
Responsibilities
Collect current neutral selfies, selfies with assigned head poses, and historical facial images from personal photo galleries. Submit at least 20 valid images, with 30 recommended to account for quality-control rejections.
Requirements
No specific education is required. Participants must submit at least 20 images that pass automated and manual quality control to qualify for payment.
Skills
- Selfie Capture
- Head-Pose Capture
- Image Collection
- Image Quality Control
- Facial Image Collection
- Attention to Detail
Visa sponsorship
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Categories
- Technology
- Data & Analytics
- Science & Research
Keywords
- Selfies
- Head-Pose Captures
- Historical Images
- Facial Images
- Image Collection
- Machine Learning
- Facial Recognition
- Model Training
- Dataset
- Image Quality Control
- Lighting Variation
- Pose Variation
- Expression Variation
- Accessories
- Aging
- Identity References