KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #deepseek-harness #typescript #ai-agent #codex #developer-tools #claude-code #windows #mcp #python #desktop-app

real-time-deep-face-recognition

★ 231 Open GitHub ↗

using facenet algorithm

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# real-time-deep-face-recogniton

Real-time face recognition program using Google's facenet.
* [youtube video](https://www.youtube.com/watch?v=T6czH6DLhC4)
## Inspiration
* [OpenFace](https://github.com/cmusatyalab/openface)
* I refer to the facenet repository of [davidsandberg](https://github.com/davidsandberg/facenet).
* also, [shanren7](https://github.com/shanren7/real_time_face_recognition) repository was a great help in implementing.
## Dependencies
* Tensorflow 1.2.1 - gpu
* Python 3.5
* Same as [requirement.txt](https://github.com/davidsandberg/facenet/blob/master/requirements.txt) in [davidsandberg](https://github.com/davidsandberg/facenet) repository.
## Pre-trained models
* Inception_ResNet_v1 CASIA-WebFace-> [20170511-185253](https://drive.google.com/file/d/0B5MzpY9kBtDVOTVnU3NIaUdySFE/edit)
## Face alignment using MTCNN
* You need [det1.npy, det2.npy, and det3.npy](https://github.com/davidsandberg/facenet/tree/master/src/align) in the [davidsandberg](https://github.com/davidsandberg/facenet) repository.
## How to use
* First, we need align face data. So, if you run 'Make_aligndata.py' first, the face data that is aligned in the 'output_dir' folder will be saved.
* Second, we need to create our own classifier with the face data we created. (In the case of me, I had a high recognition rate when I made 30 pictures for each person.)
Your own classifier is a ~.pkl file that loads the previously mentioned pre-trained model ('[20170511-185253.pb](https://drive.google.com/file/d/0B5MzpY9kBtDVOTVnU3NIaUdySFE/edit)') and embeds the face for each person. All of these can be obtained by running 'Make_classifier.py'. 
* Finally, we load our own 'my_classifier.pkl' obtained above and then open the sensor and start recognition.
 (Note that, look carefully at the paths of files and folders in all .py)
## Result
<img src="https://github.com/bearsprogrammer/real-time-deep-face-recogniton/blob/master/realtime_demo_pic.jpg" width="60%">

real-time-deep-face-recognition

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README