该资源主要包括一个基于haar特征+AdaBoost，CascadeBoost算法的人脸检测原理文档+2个AdaBoost的matlab代码，以及一个CascadeBoost的matlab代码。代码的注释很多很仔细，适合初学者。

opencv3+python人脸检测和识别- 完整实战项目源代码 识别视频《欢乐颂》中人物（附赠开课吧课程）

OpenCV人脸识别实例源码， C++ 人脸检测 人脸识别 框选 vs2013下编译通过

人脸检测matlab代码程序，用到了PCA和LDA算法原理，亲测可用

AdaBoost

百度AI图像处理(V3版本)—人脸识别（人脸对比）调用教程（基于Python3-附Demo）

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."

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

基于OpenCV的人脸检测系统设计与实现 基于OpenCV的人脸检测系统设计与实现 基于OpenCV的人脸检测系统设计与实现

用opencv提供的级联分类器进行人脸、人头检测的训练样本库，本资中包含正负样本，其中正样本10000张，负样本20000张。