面部性别检测库 [英] Face gender detection library

问题描述

我在寻找可以侦测网络摄像机流中的人脸并侦测性别的SDK或程式库。免费或付费，C ++或C＃在Windows下。
任何建议？

解决方案

我使用我的博客中的Fisherfaces方法： http://bytefish.de/blog/gender_classification ，获得了几何标准化图像的98％识别率。我为GNU Octave / MATLAB，Python和C ++提供了Fisherfaces方法的实现。

libfacerec ，用于OpenCV C ++ API（BSD许可证）的现代人脸识别库，包括Fisherfaces方法的实现。 libfacerec的最新修订版本位于：

• https://github.com/bytefish/libfacerec

libfacerec是一个CMake项目，具有良好的API ，还有一个关于性别分类的教程。您可以在以下网址查看文档的HTML版本：

• http://www.bytefish.de/dev/libfacerec/ （性别分类示例）

我提供了Fisherfaces的Python和GNU Octave / MATLAB实现： p>

• http://www.github.com/ bytefish / facerec


• http://www.bytefish.de/blog/ face_recognition_with_opencv2

要在图片中检测脸部，只需使用CascadeClassifier 与OpenCV一起出现，但不希望在未对齐的数据上获得完美的识别结果。 / p>

上面有一个答案使用Eigenfaces，它不会工作。 Eigenfaces使用主成分分析，一个不知道类的无监督算法。你必须真的很幸运，如果你嵌入你的数据的子空间区分性别。也许它通过找到最接近的图像和相关的性别产生一些积极的预测。

I'm looking for an SDK or library that can detect faces in a webcam stream, and detect gender. Free or paid, C++ or C# under Windows. Any suggestions?

解决方案

I did an experiment on gender classification with the Fisherfaces method in my blog at: http://bytefish.de/blog/gender_classification and got a 98% recognition rate for geometrically normalized images. I am providing an implementation of the Fisherfaces method for GNU Octave/MATLAB, Python and C++.

libfacerec, a modern face recognition library for the OpenCV C++ API (BSD license), includes an implementation of the Fisherfaces method. The latest revision of the libfacerec is available at:

• https://github.com/bytefish/libfacerec

libfacerec comes as a CMake project with a well-documented API, there's also a tutorial on gender classification. You can see a HTML version of the documentation at:

• http://www.bytefish.de/dev/libfacerec/ (Example on Gender Classification)

I am providing a Python and GNU Octave/MATLAB implementation of Fisherfaces at:

• http://www.github.com/bytefish/facerec
• http://www.bytefish.de/blog/face_recognition_with_opencv2

For detecting a face in an image simply use the CascadeClassifier coming with OpenCV, but don't expect perfect recognition results on misaligned data.

There's an answer above to use Eigenfaces, which won't work. Eigenfaces use a Principal Component Analysis, an unsupervised algorithm that doesn't know about classes. You must be really lucky if the subspace you embed your data into discriminates between genders. Perhaps it generates some positive predictions by finding the closest image and associated gender.

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