dlib - 19.24 交叉编译aarch64 6.5.0 版本移植，人脸聚类

Abstract—Clustering face images according to their latent identity has two important applications: (i) grouping a collection of face images when no external labels are associated with images, and (ii) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the partition algorithm. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarities between face images. This allows a dynamic selection of number of clusters and retains pairwise similarities between faces. ConPaC formulates the clustering problem as a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as k-means, spectral clustering and approximate Rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to work in a semi-supervised way that leads to improved clustering performance. We also propose an k-NN variant of ConPaC, which has a linear time complexity given a k-NN graph, suitable for large datasets. Index Terms—face clustering, face representation, Conditional Random Fields, pairwise constraints, semi-supervised clustering.

本项目通过CNN方法识别出图片集中的人脸位置并将图片裁剪，使新的图片包含尽可能多的人脸信息，然后对裁剪后的图片进行编码，使用DBSCAN方法对编码后的信息进行聚类，最终实现不同人脸的分类。 由最终的结果可知，使用DBSCAN和CNN方法进行人脸聚类具有较高的识别准确率和识别效率，且运行效果良好。

包含40个文件夹，每个文件夹是一个人，有10张图片，可用来人脸识别，人脸聚类等，用来聚类效果还是很棒的，亲测，代码过一段时间会发布到博客，学生党，也在学习中……

在数据挖掘中，最难得的就是数据集。sci14年发表的一篇经典聚类算法《Clustering by fast search and find of density peaks》里，用到了人脸聚类的数据集，并且用该算法聚得人脸的效果不错。这里便是该论文中用到的10个人，100张人脸抽取的特征向量。每一个文件表示一张人脸，从上到下，每十张属于一个类。