hrishikeshchandi/visitor-face-matching

How hrishikeshchandi/visitor-face-matching works

The product is a Streamlit visitor face-recognition app. Its main workflow accepts an image, generates an embedding, searches the FAISS index, verifies the nearest known face, maps that image filename to a requisition number, and retrieves visitor records from MySQL for display. A separate setup workflow builds the initial embeddings and index; batch uploads add embeddings to the existing index. The README documents these capabilities, while the sampled source shows the runtime calls. The graph omits unshown implementation details and external library internals.

Main entry points: main.py Streamlit app · generate_embeddings.py Initial index builder · files_handler.py Batch upload processor · config.py Known visitor images

Components 16

Visitor interface

Streamlit app main.py
Recognition results main.py
Temporary uploads config.py

Face recognition

Query embedding face_matching.py
Similarity search face_matching.py
Face verification face_matching.py
Requisition mapping face_matching.py

Visitor data

Known visitor images config.py
FAISS index config.py
Visitor records database.py
Saved embeddings config.py

Index setup and updates

Environment settings config.py
Initial index builder generate_embeddings.py
Batch upload processor files_handler.py

Other components

Visitor
MySQL database

Connections 23

Visitor → Streamlit app: uploads images README.md
Streamlit app → Temporary uploads: saves query image main.py
Streamlit app → Query embedding: requests recognition main.py
Query embedding → Similarity search: searches embedding face_matching.py
Similarity search → FAISS index: reads and searches face_matching.py
Similarity search → Face verification: checks nearest face face_matching.py
Face verification → Known visitor images: verifies against image face_matching.py
Face verification → Requisition mapping: returns verified match face_matching.py
Requisition mapping → Streamlit app: returns requisition number main.py
Streamlit app → Visitor records: requests records main.py
Visitor records → MySQL database: queries records database.py
Streamlit app → Recognition results: displays records main.py
Visitor → Initial index builder: provides dataset images README.md
Initial index builder → Known visitor images: reads image files generate_embeddings.py
Initial index builder → Saved embeddings: saves embeddings generate_embeddings.py
Initial index builder → FAISS index: builds and saves generate_embeddings.py
Streamlit app → Batch upload processor: submits batch uploads main.py
Batch upload processor → Known visitor images: adds image files files_handler.py
Batch upload processor → Saved embeddings: loads and updates files_handler.py
Batch upload processor → FAISS index: loads and updates files_handler.py
Environment settings → Known visitor images: configures location config.py
Environment settings → FAISS index: configures location config.py
Environment settings → Saved embeddings: configures location config.py

Drawn by GitDiagram on November 15, 2026 from the repository's file tree, README and source files. Read as Markdown ·

How hrishikeshchandi/visitor-face-matching works

Main entry points: main.py Streamlit app · generate_embeddings.py Initial index builder · files_handler.py Batch upload processor · config.py Known visitor images

Connections 23

Visitor → Streamlit app: uploads images README.md
Streamlit app → Temporary uploads: saves query image main.py
Streamlit app → Query embedding: requests recognition main.py
Query embedding → Similarity search: searches embedding face_matching.py
Similarity search → FAISS index: reads and searches face_matching.py
Similarity search → Face verification: checks nearest face face_matching.py
Face verification → Known visitor images: verifies against image face_matching.py
Face verification → Requisition mapping: returns verified match face_matching.py
Requisition mapping → Streamlit app: returns requisition number main.py
Streamlit app → Visitor records: requests records main.py
Visitor records → MySQL database: queries records database.py
Streamlit app → Recognition results: displays records main.py
Visitor → Initial index builder: provides dataset images README.md
Initial index builder → Known visitor images: reads image files generate_embeddings.py
Initial index builder → Saved embeddings: saves embeddings generate_embeddings.py
Initial index builder → FAISS index: builds and saves generate_embeddings.py
Streamlit app → Batch upload processor: submits batch uploads main.py
Batch upload processor → Known visitor images: adds image files files_handler.py
Batch upload processor → Saved embeddings: loads and updates files_handler.py
Batch upload processor → FAISS index: loads and updates files_handler.py
Environment settings → Known visitor images: configures location config.py
Environment settings → FAISS index: configures location config.py
Environment settings → Saved embeddings: configures location config.py

Drawn by GitDiagram on November 15, 2026 from the repository's file tree, README and source files. Read as Markdown ·