论文笔记1.2——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

二. APPROACH

In this section, we will describe our approach towards joint

face detection and alignment.

A. Overall Framework

The overall pipeline of our approach is shown in Fig. 1.

Given an image, we initially resize it to different scales to build

an image pyramid, which is the input of the following

three-stage cascaded framework:

Stage 1 : We exploit a fully convolutional network[?], called

Proposal Network (P-Net), to obtain the candidate windows

and their bounding box regression vectors in a similar manner

as [29]. Then we use the estimated bounding box regression

vectors to calibrate the candidates. After that, we employ

non-maximum suppression (NMS) to merge highly overlapped

candidates.

Stage 2 : all candidates are fed to another CNN, called Refine

Network (R-Net), which further rejects a large number of false

candidates, performs calibration with bounding box regression,

and NMS candidate merge.

Stage 3 : This stage is similar to the second stage, but in this

stage we aim to describe the face in more details. In particular,