DESCRIPTION
While all sorts od Cloud APIs are becoming more and more popular in replacing local-based alternatives, this project was intented to demonstrate Face Recogition via Cloud, with OpenCV's Haar Classificator running locally and is used for detecting faces.
Project implements two optional Cloud APIs - Face++ and FaceR. Recognition is done comparing locally detected face (using OpenCV) with person model pre-trained by few hundred images (total 505 images were used in a process using Cloud APIs). Result of a Face recognition is confidence level which tells us what are the odds that unknown, detected face really belongs to the pretrained person model. Threshold of 70% is set for recognition to be successful.
PREREQS (4)
1.) Obtain your API credentials
To use Face++ API, obtain keys and do initial setting, follow this link: http://www.faceplusplus.com/create-a-new-app/
To use FaceR API, Mashape API provider is used. Therefore, obtaining keys and initial settings is done on through Mashape.
Link: https://market.mashape.com/animetrics/animetrics-face-recognition
2.) New face model needs to be created and trained within a group, using each API separate.
3.) After doing previous steps, edit facer.py and faceplusplus scripts by replacing:
OBTAINED KEYS (For facer.py, edit "api_key" and "X-Mashape-Key" variables; For faceplusplus.py, edit "API_KEY" and "API_SECRET" variables),
Person name and group name (search for and replace all "PERSON_NAME" with new trained model name, "GROUP_NAME" with new group name.
4.) INSTALL
OpenCV 2.4.10 or lower
Python 2.7
Unirest
ImageTk
RUN PROGRAM
1.) Open terminal
2.) Go to project directory (readme.txt location folder)
3.) Type following command: python face_recognition_GUI.py
NOTE
To use Face++ SDK for testing purposes, you need to edit API keys in apikey.cfg file.
To start SDK, enter the following command in the project directory:
"python cmdtool.py".
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