|本期目录/Table of Contents|

[1]雷明仪,苏松志*,李绍滋.卡口场景下人脸检测模型的自适应重训练算法[J].厦门大学学报(自然科学版),2017,56(03):429-436.[doi:10.6043/j.issn.0438-0479.201611054]
 LEI Mingyi,SU Songzhi*,LI Shaozi.An Adaptive Retraining Algorithm for Face Detection Models in the Scene of Station Ticket Barriers[J].Journal of Xiamen University(Natural Science),2017,56(03):429-436.[doi:10.6043/j.issn.0438-0479.201611054]
点击复制

卡口场景下人脸检测模型的自适应重训练算法(PDF/HTML)
分享到:

《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
56卷
期数:
2017年03期
页码:
429-436
栏目:
研究论文
出版日期:
2017-05-24

文章信息/Info

Title:
An Adaptive Retraining Algorithm for Face Detection Models in the Scene of Station Ticket Barriers
文章编号:
0438-0479(2017)03-0429-08
作者:
雷明仪苏松志*李绍滋
厦门大学信息科学与技术学院,福建厦门361005
Author(s):
LEI MingyiSU Songzhi*LI Shaozi
School of Information Science and Engineering,Xiamen University,Xiamen 361005,China
关键词:
人脸检测卡口场景重训练聚合通道特征模型
Keywords:
face detectionstation ticket barriersretrainingaggregate channel feature model
分类号:
TP 181
DOI:
10.6043/j.issn.0438-0479.201611054
文献标志码:
A
摘要:
卡口场景下的人脸检测是视频智能监控的关键技术.然而,由于不同的人脸数据集的样本分布之间存在差异,在现有公开数据集上训练得到的人脸检测模型在卡口场景下难以取得令人满意的效果.为了解决上述问题,构建了一个卡口场景下的人脸数据集,并提出了一种简单且有效的模型重训练方法.该重训练方法能在模型检测人脸时,自适应地选取新的训练样本进行模型的重训练.在卡口场景测试集上的实验结果表明,该重训练方法能明显降低聚合通道特征模型的平均漏检率.
Abstract:
Face detection,in videos of passengers going through station ticket barriers,is a fundamental step of the intelligent video surveillance.However,since face data from different datasets follow different distributions,models trained on publicface benchmarks fail to obtain satisfying results in the scene of station ticket barriers.To solve this problem,we first construct our own face dataset in this special scene,and then propose a simple and effective retraining strategy.This strategy selfadaptively selects new samples to retrain a new model when a model is detecting faces.Experiments on test set from the scene of ticket barriers show that this strategy significantly reduces the logaverage miss rate of aggregate channel feature model,demonstrating the effectiveness of our retraining approach.

参考文献/References:

[1] VIOLA P,JONES M.Robust real-time face detection[J].International Journal of Computer Vision,2004,57(2):137-154.
[2] OJALA T,PIETIK?INEN M,M?ENP?? T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2002,24(7):971-987.
[3] HE N,CAO J,SONG L.Scale space histogram of oriented gradients for human detection[C]∥International Symposium on Information Science and Engineering.Shanghai:IEEE,2008:167-170.
[4] LI J,ZHANG Y.Learning SURF cascade for fast and accurate object detection[C]∥Computer Vision and Pattern Recognition.Portland:IEEE,2013:3468-3475.
[5] Xiao R,Zhu L,Zhang H J.Boosting chain learning for object detection[C]∥IEEE International Conference on Computer Vision.Nice:IEEE,2003:709-715.
[6] WU B,AI H,HUANG C,et al.Fast rotation invariant multi-view face detection based on real adaboost[C]∥IEEE International Conference on Automatic Face and Gesture Recognition.Seoul:IEEE,2004:79-84.
[7] CHEN D,REN S,WEI Y,et al.Joint cascade face detection and alignment [C]∥European Conference on Computer Vision.Zurich:Springer International Publishing,2014:109-122.
[8] BOURDEV L,BRANDT J.Robust object detection via soft cascade[C]∥IEEE International Conference on Computer Vision and Pattern Recognition.San Diego:IEEE,2005,2:236-243.
[9] 严严,陈日伟,王菡子.基于深度学习的人脸分析研究进展[J].厦门大学学报(自然科学版),2017,56(1):13-24.
[10] YANG B,YAN J,LEI Z,et al.Aggregate channel features for multi-view face detection[C]∥IEEE International Joint Conference on Biometrics(IJCB).Clearwater:IEEE,2014:1-8.
[11] 北京旷视科技有限公司.Face++[EB/OL].[2016-11-01].http:∥www.faceplusplus.com/.
[12] DOLLAR P,APPEL R,BELONGIE S,et al.Fast feature pyramids for object detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,36(8):1532-1545.
[13] LIU Z,LUO P,WANG X,et al.Deep learning face attributes in the wild[C]∥IEEE International Conference on Computer Vision.Santiago:IEEE,2015:3730-3738.
[14] YU S Q.Libfacedetection:a library for face detection in images[EB/OL].[2016-11-01].https:∥github.com/ShiqiYu/libfacedetection.

备注/Memo

备注/Memo:
收稿日期:2016-11-25 录用日期:2017-02-18
基金项目:国家自然科学基金(61572409,61402386,61571188); 福建省2011协同创新中心项目(闽教科〔2015〕75号)
*通信作者:ssz@xmu.edu.cn
引文格式:雷明仪,苏松志,李绍滋.卡口场景下人脸检测模型的自适应重训练算法[J].厦门大学学报(自然科学版),2017,56(3):429-436.
Citation:LEI M Y,SU S Z,LI S Z.An Adaptive re-training algorithm for face detection models in the scene of station ticket barriers[J].J Xiamen Univ Nat Sci,2017,56(3):429-436.(in Chinese)
更新日期/Last Update: 1900-01-01