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PyFaces

B2CReconocimiento FacialPython CLIAutomatización
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>90 %Time Saved
2 HoursDelivery Speed

Context

The project was born from the real need of a former teacher and event photographer. After covering graduations or weddings, he faced the tedious manual task of separating and grouping photos by person to deliver them to his clients.

The User's Problem

The immense time lost in visual sorting. In a graduation with 30 students and 700 photos, the photographer had to review the same gallery 30 times. This consumed entire days of exhausting post-production work.

The Experience Created

I developed a Python engine based on facial recognition, packaged in Docker for frictionless installation. Given 3 reference photos, the algorithm scans the entire batch, compares the numerical proximity of the faces, and extracts all matches to a new folder.

Community Impact

A monumental reduction in delivery times. Processing and identifying people in a batch of 700 images went from taking 3 to 4 days, to being completed autonomously in just 2 hours.

Tech Stack

Python
Docker
OpenCV / dlib

My Role:

Solo Software Developer