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

License: MIT License

Objective-C 48.50% Objective-C++ 49.64% C++ 1.63% Ruby 0.23%

ios-mobilefacenet-mtcnn-faceantispoofing's Introduction

MobileFaceNet-iOS

This project includes three models.

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

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

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

Android platform implementation: https://github.com/syaringan357/Android-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 project, remember to add .tflite file in Build Phases -> Copy Bundle Resources

SCREEN SHOT

ios-mobilefacenet-mtcnn-faceantispoofing's People

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prashanthbn amrgen81 akshitverma ngtrphuong abdalwhabmalkawi syzhou1223 armangorjipoor

ios-mobilefacenet-mtcnn-faceantispoofing's Issues

Need swift language support

when you will provide swift language support?

Poor liveness score on sample app

I seem to be getting 0.99 for my real face.

camera support plan?

In your android example, you develop camera version. but iOS version, only have two images.
do u have any plan to develop or update camera to this iOS app ? please....
this is best example.

Swift Language Support

Hi, thanks for your nice library
When you will provide the swift language support?

静态图片识别有两个问题

1：mtncc对有旋转角度的图片人脸检测效果很差，有时会把一张脸识别出几个Box，有时候又完全识别不出来
2：mtncc检测后没有做人脸对齐，直接裁切后使用facenet做距离对比效果很差
对于第一点，iOS自带的Vision框架不管是性能还是检测结果都甩mtncc10条街，但是扫描后裁剪后转换成openCv图片对象后如何做对齐还不是很清楚，希望能探讨一下

人脸检测的时候内存暴涨

你好，你这边有遇到人脸检测的时候内存暴涨的情况吗，有优化吗

english version

hi, thanks you for great lib, do u provide english version?

Weird problem with camera on portrait mode

On the iOS, in portrait mode it seems it has more problems correctly identifying a real face or an image. I would find the face, crop the face, and it say 0.99 for a real face. in landscape mode. it works much better

Swift support?

@syaringan357 Do you have this project in swift? Or supported for XCode 11.2.1? If you can provide MTCNN, Box, Tools, ios_image_load files in swift that would be great.

Domain=org.tensorflow.lite.interpreter Code=1 "Input tensor at index (0) expects data size (27648), but got (0)." UserInfo={NSLocalizedDescription=Input tensor at index (0) expects data size (27648), but got (0).}

Domain=org.tensorflow.lite.interpreter Code=1 "Input tensor at index (0) expects data size (27648), but got (0)." UserInfo={NSLocalizedDescription=Input tensor at index (0) expects data size (27648), but got (0).} 检测人脸的时候报错，请问是何原因

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