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Data Collection

Image Data Collection for a Palm Recognition Task

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Collecting 20,000 standardized photos of people's palms sounds simple until you try doing it in practice.

Prolific’s reach and filtering features made the scale achievable, while the practical work required disciplined handling of logistics, costs, data verification, and platform-specific constraints.

Industry Biometrics
Timeline 2 months
Data 20,000 palm photo sets
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Industry Biometrics
Timeline 2 months
Data 20,000 palm photo sets

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

  • 01

    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.

  • 02

    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.

  • 03

    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.

The Result

  • Collected 20,000 palm sets and launched the next batch of 20,000

  • Delivered a fully verified dataset ahead of schedule with consistently high quality

  • Scaled the process while keeping predictable speed and global coverage, with unified standards regardless of region, device or platform

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