这是卡内基梅隆大学的公开人脸测试集。测试集共分四个文件夹，分别为测试集A、B、C和旋转测试集。由于大小限制，因此将四个文件夹分开上传 The image dataset is used by the CMU Face Detection Project and is provided for evaluating algorithms for detecting frontal views of human faces. This particular test set was originally assembled as part of work in Neural Network Based Face Detection. It combines images collected at CMU and MIT. Please give appropriate acknowledgements when you use these test sets. In the lists of files below, you will find references to Test Sets A, B, C and the Rotated Test Set. Test Set B was provided by Kah-Kay Sung and Tomaso Poggio at the AI/CBCL Lab at MIT, and Test Sets A,C and the rotatated test set were collected here at CMU (by Henry A. Rowley, Shumeet Baluja, and Takeo Kanade). In [Schneiderman and Kanade, 2000] and [Schneiderman and Kanade, 1998] we refer to the combination of test sets A, B, and C as the "combined test sets of Sung and Poggio and Rowley, Baluja, and Kanade." In [Rowley, Baluja, and Kanade, 1998] we refer to the combination of sets A, B, C as "test set one" and in [Rowley, Baluja, and Kanade, 1997] we refer to it as the "upright set" as distinguished from the "rotated set."

这是卡内基梅隆大学的公开人脸测试集。测试集共分四个文件夹，分别为测试集A、B、C和旋转测试集。由于大小限制，因此将四个文件夹分开上传 The image dataset is used by the CMU Face Detection Project and is provided for evaluating algorithms for detecting frontal views of human faces. This particular test set was originally assembled as part of work in Neural Network Based Face Detection. It combines images collected at CMU and MIT. Please give appropriate acknowledgements when you use these test sets. In the lists of files below, you will find references to Test Sets A, B, C and the Rotated Test Set. Test Set B was provided by Kah-Kay Sung and Tomaso Poggio at the AI/CBCL Lab at MIT, and Test Sets A,C and the rotatated test set were collected here at CMU (by Henry A. Rowley, Shumeet Baluja, and Takeo Kanade). In [Schneiderman and Kanade, 2000] and [Schneiderman and Kanade, 1998] we refer to the combination of test sets A, B, and C as the "combined test sets of Sung and Poggio and Rowley, Baluja, and Kanade." In [Rowley, Baluja, and Kanade, 1998] we refer to the combination of sets A, B, C as "test set one" and in [Rowley, Baluja, and Kanade, 1997] we refer to it as the "upright set" as distinguished from the "rotated set."

主要为大家详细介绍了Python OpenCV利用笔记本摄像头实现人脸检测，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

数据来自kinect生成的图像

压缩包为代码资源，通过django搭建的人脸检测web平台，以单独的api接口形式对外提供调用，自测可用。

face_gui 项目介绍 该项目能够从给定的正面照片中，自动识别脸部区域，并对图像前景进行提取分割，然后替换背景，并按照证件照的规格进行规范化处理。一共由5个模块组成： 1. 头部局部照识别与截取模块设计 利用Vahid Kazemi 和 Josephine Sullivan提出的基于gradient boosting的回归树算法检测面部的68个关键点位置. 2. 图像前景分割 利用opencv中的grabcut方法对人脸周围区域进行分割，将人头和衣服等前景分离出来. 3. 证件照规范化 按照规格进行图片处理:分辨率：361×381，分辨率96dpi，位深度24，大小30k左右. 4. 背景替换 根据图片的背景颜色特征进行替换背景（蓝-红-白），如蓝背景变为红背景：将BGR图像转为HSV图像，蓝颜色H通道在78和110之间，然后转换通道将这些像素替换为(0，0，255)即可。 5. 界面

使用 Viola-Jones 算法进行检测。 要保存裁剪的图片，您需要更改文件夹位置。

Matlab编写的实时检测单人脸和多人脸，压缩包内有GUI界面和检测用的视频。

C#调用百度SDK实现 winform界面,关于其中的appkey需要自行到百度控制台查看自己的key,非实时检测人脸搜索

人脸识别证件照批量裁剪软件是我自己找人开发的 对学校、影楼要批量处理证件照等人来说非常有用，可以大大提高效率 注意：加载文件夹需要等几秒钟后才能点击开始批量裁剪，操作太快可能引起错误。