Aprar

faceserver

Face identification and recognition scalable server with multiple face directories. https://github.com/ehp/faceserver

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Face recognition technology demo

Mass faces identification and recognition in images.

Installation

The simplest complete installation is docker compose: docker-compose up -d in root directory. For detailed installation instructions look at API server or vectorizer readme files. Without nvidia docker support docker runs only on cpu with very degraded performance (over minute on 6 cpu cores).

Usage

Learn people faces

curl -X POST -F 'person=PID' -F 'directory=DIR' -F 'file=@portrait.jpg' http://localhost:8080/learn

Replace PID with person's id (e.g. database id or name) and DIR with your directory name (e.g. company name). People are recognized only within same directory. png or jpeg images are supported. Only images with one face are allowed for learning ! Usually only one good portrait photo is enough but you can learn more photos for each person.

Recognize people

curl -X POST -F 'directory=DIR' -F 'file=@photo.jpg' http://localhost:8080/recognize

Replace DIR with your directory name (e.g. company name). People are recognized only within same directory. For each detected face the most probable person's id is returned. png or jpeg images are supported.

Example result:

{
"status":"OK",
"url":"/files/00636b47-e6a5-4fab-8a02-9e44d052c193.jpg",
"filename":"photo.jpg",
"directory":"mydir",
"persons":[
{"id":"PID1","box":[2797,1164,2918,1285],"score":0.999998927116394,"probability":0.8342},
{"id":"PID2","box":[2398,1854,2590,2046],"score":0.9999780654907227,"probability":0.32546},
{"id":"PID3","box":[1753,1148,1905,1300],"score":0.9999217987060547,"probability":0.65785}
]}

Field	Description
status	Status message - either OK or error text
url	Relative url to original image
filename	Original image filename
directory	Directory name
persons	Recognized people array
id	Person's id
box	Box around face
score	Face detection score (i.e. probability)
probability	Person recognition probability

Architecture

This demo consist of three parts - API server, vectorizer and database. API server is frontend server written in golang. Vectorizer is the main part which identifies faces and creates vectors from faces. Database is simple storage for learned vectors. Both API server and vectorizer are fully scalable e.g. in kubernetes. The only non scalable part is postgresql database but it can be easily replaced with different storage - e.g. HBase. Just reimplement storage.go in API server. Only API server listen to customer requests, the rest are internal components and should not be directly accessible from internet.

Future roadmap

Training on identified faces (both nets are trained separately now)
Face alignment between identification and recognition
Web user interface (help needed !)

Based on

Github repositories:

Papers:

Licensing

Code in this repository is licensed under the Apache 2.0. See LICENSE.

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