ACF目标检测算法论文

Aggregate Channel Features for Multi-view Face Detection

Bin Yang Junjie Yan Zhen Lei Stan Z. Li

∗

Center for Biometrics and Security Research & National Laboratory of Pattern Recognition

Institute of Automation, Chinese Academy of Sciences, China

yb.derek@gmail.com {jjyan,zlei,szli}@nlpr.ia.ac.cn

Abstract

Face detection has drawn much attention in recent

decades since the seminal work by Viola and Jones. While

which extends the image channel to diverse types like gradi-

ent magnitude and oriented gradient histograms and there-

fore encodes rich information in a simple form. We adopt

a novel variant called aggregate channel features, make

a full exploration of feature design, and discover a multi-

scale version of features with better performance. To deal

with poses of faces in the wild, we propose a multi-view

detection approach featuring score re-ranking and detec-

tion adjustment. Following the learning pipelines in Viola-

Jones framework, the multi-view face detector using ag-

computer interaction. In the past decade, the most influen-

tial work should be the face detection framework proposed

by Viola and Jones [22]. The Viola-Jones (abbreviated as

VJ below) framework uses rectangular Haar-like features

and learns the hypothesis using Adaboost algorithm. Com-

bined with the attentional cascade structure, the VJ detector

achieved real-time face detection at that time. Despite the

great success of the VJ detector, the performance is still far

from satisfactory due to the large appearance variance of

faces in unconstrained settings.