StepPrint
Biometric foot-recognition research prototype — exploring feature extraction and matching for foot-based identification.
TODO: Add permission-approved, sanitized product screenshots. Concept art is not shown as product evidence.
Technical concept illustration. The case study below describes the project implementation.
StepPrint is a research prototype exploring foot-print biometrics as an identification method — an uncommon alternative to the face and fingerprint recognition most biometric systems default to. Foot-based biometrics have far less established tooling and research behind them, so the feature-extraction and matching pipeline had to be built largely from first principles rather than adapted from an off-the-shelf model: image preprocessing to normalize input, key-point and shape-based feature extraction, and a similarity-based matching algorithm for comparing samples.
Validated against a small labeled evaluation set to measure real accuracy rather than relying on qualitative impressions — a working proof-of-concept demonstrating the feasibility of foot-based biometric matching as a research direction.
Related work
DocVerify — Photo ID Compliance
AI tool validating and processing government-issued photo ID documents — OCR extraction and compliance checks against formatting rules.
VisageLab
Computer-vision face-swap research tool built on InsightFace — experimented with realistic face-swapping pipelines for creative applications.
GuestGallery
Python app using face recognition to allocate 2000+ wedding photos to guests. Finds each person across photos and surfaces “here are your pictures.” Processes large image sets efficiently.
Interested in similar work?
Review my services and how we can define a useful first milestone.
Discuss similar work