Face recognition using facenet

1. Intro

Facenet is developed by Google in 2015, the result of the net is the Euclidean embedding of human face.

By careful defined triplet loss function, facenet achieves high accuracy on LFW(0.9963) and FacesDB(0.9512).

Darknet is a fast, easy to read DL framework. Yolo is running based on it.

2. Dependencies

OpenCV for video i/o, face detection, image resizing, warping, and 3D pose estimation.

Dlib for facial landmark detection.

NNPACK for faster neural network computations.

Zenity for text input.

3. Installation and run

sudo apt-get install zenity
cd facenet-darknet-inference
#edit makefile
#specify your OPENCV_HEADER_DIR, OPENCV_LIBRARY_DIR, DLIB_HEADER_DIR, DLIB_LIBRARY_DIR, NNPACK_HEADER_DIR, NNPACK_LIBRARY_DIR
make
mkdir data
cd data
touch name
cd ..
mkdir model

download weights and extract in facenet-darknet-inference folder

cd facenet-darknet-inference
./facenet-darknet-inference

4. Note

OpenCV VJ face + Dlib landmark detection is used rather than MTCNN. VJ method is faster, but the unstable cropping may slightly influence recognition accuracy.

KNN is the final classification method, but it is suffered for openset problem. The 1792-d feature before bottleneck layer with normalization is used for KNN, because it has better result in openset than original facenet model, but you can still try the original network configure yourself just replacing facenet.cfg to facenet_full.cfg

The facenet.weight is converted from facenet inception-resnet v1 20180402-114759 model

5. Result