Face_recognition
🍎 My own face recognition with deep neural networks.
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人脸识别
这个仓库是使用TensorFlow 2.0框架，并基于 cvpr2019-arcface 论文上完成的，其中主要分为四大块：人脸检测、人脸矫正、提取特征和特征比对。各个模块的大小和在我的 17 款 macbook-pro 的 CPU 上跑耗时如下：
- 人脸检测：使用的是 mtcnn 网络，模型大小约 1.9MB，耗时约 30ms；
- 人脸矫正：OpenCV 的仿射变换，耗时约 0.83ms；
- 提取特征：使用 MobileFaceNet（或IResNet）网络，耗时约30ms；
- 特征比对：使用曼哈顿距离，单次搜索和完成比对耗时约 0.011 ms；
注册人脸
注册人脸的方式有两种，分别是:
- 打开相机注册:
$ python register_face.py -person Sam -camera
按 s 键保存图片，需要在不同距离和角度拍摄 10 张图片或者按 q 退出。
- 导入人脸图片:
保证文件的名字与注册人名相同，并且每张图片只能出现一张这个 ID 的人脸。
$ python register_face.py -person Jay
识别人脸
| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) | cpu-time | weights |
|---|---|---|---|---|---|---|
| MobileFaceNet | 99.50 | 88.94 | 95.91 | --- | 35ms | 下载链接 |
| IResNet | 99.77 | 98.27 | 98.28 | 98.47 | 435ms | 提取码: xgmo |
识别模型用的是 MobileFaceNet 网络，这里直接使用了 insightface 在 ms1m-refine-v1 三百万多万张人脸数据集上训练的模型。这部分工作在 mxnet 分支上，你可以通过 git checkout mxnet 进行切换。
由于该模型是 mxnet 格式，因此使用了 mmdnn 导出了其模型权重 mobilefacenet.npy。接着使用了 TF2 自己手写了一个 MobileFaceNet 网络并导入权重，预测精度没有任何损失。这部分工作在 master 分支上。
最后，如果你要识别人脸，可以执行：
$ python main.py
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Mtcnn