tiqq111 / android-mobilefacenet-mtcnn-faceantispoofing

Use tensorflow Lite on Android platform, integrated face detection (MTCNN), face anti spoofing (CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing) and face comparison (MobileFaceNet use InsightFace loss)

MobileFaceNet-Android

This project includes three models.

MTCNN(pnet.tflite, rnet.tflite, onet.tflite), input: one Bitmap, output: Box. Use this model to detect faces from an image.

FaceAntiSpoofing(FaceAntiSpoofing.tflite), input: one Bitmap, output: float score. Use this model to determine whether the image is an attack.

MobileFaceNet(MobileFaceNet.tflite), input: two Bitmaps, output: float score. Use this model to judge whether two face images are one person.

iOS platform implementation: https://github.com/syaringan357/iOS-MobileFaceNet-MTCNN-FaceAntiSpoofing

References

https://github.com/vcvycy/MTCNN4Android
This project is the Android implementaion of MTCNN face detection.

https://github.com/davidsandberg/facenet
Use the MTCNN here to convert .tflite, so that you can adapt to any shape.

https://github.com/jiangxiluning/facenet_mtcnn_to_mobile
Here's how to convert .tflite.

https://github.com/yaojieliu/CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing
Face Anti-spoofing. I trained FaceAntiSpoofing.tflite, which only supports print attack and replay attack. If you have other requirements, please use this source code to retrain.

https://github.com/sirius-ai/MobileFaceNet_TF
Use this model for face comparison on mobile phones because it is very small.

BUILD

After putting .tflite in your assets directory, remember to add this code to your gradle:
aaptOptions {
　　noCompress "tflite"
}

SCREEN SHOT