Automated Image Filtering Using Face Recognition
RPA-style pipeline for automated photo sorting
Technologies Used
Executive Overview
An automated image filtering tool that detects and matches faces in a photo collection against reference profiles, then sorts the results without manual review.
The Problem Statement
Manually sorting large batches of photos to find images containing specific people is slow and error-prone, especially for event or bulk photo collections.
The Engineering Solution
Designed the solution like an RPA pipeline — input, processing, decision-making, and output stages — using face detection and recognition to match images against reference profiles, then automatically moving matched images into structured output folders.
System Architecture
Python script using OpenCV for image preprocessing and InsightFace for face detection and recognition, with NumPy for similarity scoring and a file-system automation layer that moves matched files into organized output directories.
Technical Challenges
Tuning face-match confidence thresholds to minimize false positives/negatives across photos with varying lighting, angles, and image quality.
Key Lessons Learned
Treating a computer vision task as an explicit RPA-style pipeline (input → process → decide → output) made the logic much easier to test and extend than a single monolithic script.
Roadmap & Future Improvements
Adding a simple review UI for borderline matches and batch processing support for larger photo archives.