文件列表:

Dockerfile (302, 2022-08-04)
app (0, 2022-08-04)
app\.env (113, 2022-08-04)
app\app.py (4129, 2022-08-04)
app\client.py (1354, 2022-08-04)
create_facebank.py (2123, 2022-08-04)
data (0, 2022-08-04)
data\checkpoints (0, 2022-08-04)
data\images (0, 2022-08-04)
data\images\reynolds_001.png (632139, 2022-08-04)
data\images\reynolds_002.png (613505, 2022-08-04)
data\images\reynolds_003.png (428775, 2022-08-04)
data\reynolds.csv (32233, 2022-08-04)
facetools (0, 2022-08-04)
facetools\__init__.py (185, 2022-08-04)
facetools\face_detection.py (1086, 2022-08-04)
facetools\face_recognition.py (1850, 2022-08-04)
facetools\liveness_detection.py (1852, 2022-08-04)
facetools\utils.py (2591, 2022-08-04)
requirements.txt (356, 2022-08-04)
setup.py (219, 2022-08-04)
webcam_test.py (1292, 2022-08-04)

# face-recognition-liveness Face liveness detection and indentity recognition using fast and accurate convolutional neural networks is implemented in Pytorch. Also a Flask API and ready-to-use Dockerfile can be found in this repository. This project uses [Mediapipe](https://github.com/google/mediapipe) for face detection and the face recognition model is borrowed from [facenet-pytorch](https://github.com/timesler/facenet-pytorch) .The liveness detection model came from [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://arxiv.org/abs/1907.04047) paper and, [pre-trained models](https://www.idiap.ch/software/bob/docs/bob/bob.paper.deep_pix_bis_pad.icb2019/master/pix_bis.html#using-pretrained-models) are published by authors.  ## Getting Started Download .onnx models and put them in `data/checkpoints` folder. - [InceptionResnetV1_vggface2.onnx](https://github.com/ffletcherr/face-recognition-liveness/releases/download/v0.1/InceptionResnetV1_vggface2.onnx) - [OULU_Protocol_2_model_0_0.onnx](https://github.com/ffletcherr/face-recognition-liveness/releases/download/v0.1/OULU_Protocol_2_model_0_0.onnx) >*Note:* If you have an internet connection, models will be downloaded automatically. ## Simple Usage Run the following command to check liveness (and test are you Ryan Reynolds or not!) ```bash $ python webcam_test.py ``` >*Note:* Liveness score is between 0 and 1 and, in average, it is enough be greater than ~ **0.03** to be considered as a live image. ## Create Facebank CSV In the first step you need a facebank. So put some images (jpg, jpeg, png) in a folder and create facebank csv file using `create_facebank.py` script: ``` $ python3 create_facebank.py --images ./data/images \ --checkpoint ./data/checkpoints/InceptionResnetV1_vggface2.onnx \ --output ./data/test.csv ``` --images: the path to the images folder --checkpoint: the path to the resnet vggface2 onnx checkpoint --output: the path to the output csv file ## Run Docker Now you can start the deployment process. Variables (models and facebank names) can be changed in `app/.env` file: ``` DATA_FOLDER=data RESNET=InceptionResnetV1_vggface2.onnx DEEPPIX=OULU_Protocol_2_model_0_0.onnx FACEBANK=test.csv ``` First build the docker image: ``` $ sudo docker build --tag face-demo . ``` Now run the image as a container: ``` $ sudo docker run -p 5000:5000 face-demo python3 -m flask run --host=0.0.0.0 --port=5000 ``` ### Test Finally we can test our app using a python client. So for testing just run this: ``` # face-recognition-liveness/ $ cd ./app $ python3 client.py --image ../data/images/reynolds_001.png --host localhost --port 5000 --service main ```

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