Image Data Collection for a Palm Recognition Task

The Task

The client was building an AI-powered biometric system for banking terminals, designed to ensure secure customer authentication and payment processing through fingerprint recognition. To train and validate the underlying biometric algorithms, the project required a large, high-quality dataset consisting of 20,000 palm image sets.

Each set had to include six strictly standardized photos, three taken with the front camera and three with the camera on a mobile phone.

At this scale, broad geographic coverage and device diversity were also critical. At the same time, we had to keep the cost within a fixed budget, which turned the project into an exercise in precise traffic and workflow optimization.

The Solution

Multi-platform strategy

We launched data collection across several international platforms. The main volume came from Prolific as it provides fast access to a global audience, high-quality responses and flexible participant filtering.

Process organization and quality control

Even at high volume, attention to detail had to stay consistent at every step. We:
• created step-by-step instructions to ensure correct photo capture
• set up automatic format checks
• monitored diversity and representativeness
• controlled cost and collection speed

The goal was not only to gather photos, but to build a controlled and scalable process that would maintain quality at every stage.

Audience management and filtering

Prolific’s filtering capabilities played a key role. They allowed us to:
• select participants with the right parameters
• keep upload quality consistently high
• maintain a stable and predictable data flow

Clear task structure and engaged respondents helped us deliver results ahead of schedule, roughly 10% faster than planned.