基于LARK特征提取的AdaBoost人脸检测系统

摘要/Abstract

摘要：

由于图像易受到外部条件及图像背景的影响,AdaBoost人脸检测方法在特征分类的过程中,单个分类器存在将人脸图像误判为非人脸图像的情况,致使分类器在检测过程中存在误差。文中通过研究AdaBoost人脸检测方法,并将LARK特征提取方法应用到特征分类当中,使原有的方法上得到改进,从而有效提高了人脸检测的准确度。

关键词: 人脸检测, 特征提取, LARK, 特征分类, AdaBoost算法

Abstract:

As the images are vulnerable to external conditions and image background in the imaging,some non-face images are classified as face images by a single classifier in AdaBoost face detection.And for feature classification,it is difficult to detect face images by using a single classifier.This paper studies the AdaBoost face detection and uses LARK for feature extraction to construct a face system,which efficiently improves the accuracy of face detection.

Key words: face detection;feature extraction;LARK;feature detection;AdaBoost algorithm

中图分类号:

TP391.41

王寅. 基于LARK特征提取的AdaBoost人脸检测系统[J]. , 2014, 27(7): 137-.

WANG Yin. AdaBoost Face Detection Based on LARK Feature Extraction[J]. , 2014, 27(7): 137-.

参考文献

［1］FREUND Y,SCHAPIRE R E.A decision-theoretic generalization of on-line learning and an application to boosting［J］.Journal of Computer and System Sciences,1997,55(1):119-139.

［2］SEO H J,MILANFAR P.Training-free,generic object detection using locally adaptive regression kernels［J］.Pattern Analysis and Machine Intelligence,2010,32(9):1688-1704.

［3］VIOLA P,JONES M.Robust real-time object detection［J］.International Journal of Computer Vision,2004,57(2):137-154.

［4］MASNADI-SHIRAZI H,VASCONCELOS N.High detection-rate cascades for real-time object detection［C］.CA USA:ICCV,2007.

［5］LIENHART R,MAYDT J.An extended set of haar-like features for rapid object detection［C］.MA USA:IClP,2002.

［6］VIOLA P,JONES M J.Rapid object detection using a boosted cascade of simple features［C］.Shanghai:Computer Vision and Pattern Recognition,2001.

［7］TAKEDA H,P.VAN BEEK,MILANFAR P.Spatio-temporal video interpolation and denoising using Motion-Assisted Steering Kernel(MASK) regression［C］.Guangzhou:International Conference on Image Processing,2008.

［8］TAKEDA H,FARSIU S,MILANFAR P.Kernel regression for image processing and reconstruction［J］.IEEE Transactions on Image Processing,2007,16(2):349-366.

［9］SEO H J,MILANFAR P.Deblurring using regularized locally adaptive kernel regression［J］.IEEE Transactions on Image Processing,2008,17(4):550-563.

［10］ESCOUFIER Y.Operator related to a data matrix:a survey［C］.Rome:Compstat 2006-Proceedings in Computational Statistics,2006.

［11］SEO H J,MILANFAR P.Face verification using the lark face representation［J］.Information Forensics and Security,2011,6(4):1275-1286.

［12］YANG Minghsuan,DAVID J K.Dececting faces in images:a survey［J］.Pattern Analysis and Machine Intelligence,2002,24(1):34-58.

［13］TIEU K,VIOLA P.Boosting image retrieval［C］.London:Computer Vision and Pattern Recognition,2000.

[1]	伍伟嘉,杨俭,袁天辰,邵志慧. 基于支持向量机的轨道结构病害识别研究[J]. 电子科技, 2022, 35(2): 27-33.
[2]	李辉,王一丞. 基于CNNCIFG-Attention模型的文本情感分类[J]. 电子科技, 2022, 35(2): 46-51.
[3]	邵志慧,杨俭,袁天辰,伍伟嘉. 基于排列熵和支持向量机的轨枕病害诊断[J]. 电子科技, 2022, 35(2): 52-58.
[4]	司明明,陈玮,胡春燕,尹钟. 融合Resnet50和U-Net的眼底彩色血管图像分割[J]. 电子科技, 2021, 34(8): 19-24.
[5]	马立新,豆晨飞,宋晨灿,杨天笑. 基于特征融合卷积神经网络的绝缘子无损检测[J]. 电子科技, 2021, 34(7): 26-30.
[6]	刘姝,邵杰,张颐婷,张善章. 基于双谱和谱特征的心电信号分类方法[J]. 电子科技, 2021, 34(5): 42-46.
[7]	宋章明,贺慧勇,黄跃俊. 扁平漆包线表面缺陷在线检测研究[J]. 电子科技, 2021, 34(5): 72-78.
[8]	葛靖,刘子龙. 基于CNN和LSTM的睡眠呼吸暂停检测算法[J]. 电子科技, 2021, 34(2): 21-26.
[9]	巨志勇,翟春宇,张文馨. 基于SVM与区域生长的彩色商品标签图像分割方法[J]. 电子科技, 2021, 34(10): 69-74.
[10]	杨思燕,苗凯彬,王锋,苗启广. 视频图像中人脸自动检测与统计算法[J]. 电子科技, 2020, 33(8): 1-9.
[11]	王春江,张猛,张建飞. 基于网络摄像头的活体人脸识别系统设计[J]. 电子科技, 2020, 33(6): 63-68.
[12]	刘义民,刘韬,陈庆. CHMM和AR模型在轴承性能退化评估与预测中的应用[J]. 电子科技, 2020, 33(5): 58-65.
[13]	司琴,李菲菲,陈虬. 基于深度学习与特征融合的人脸识别算法[J]. 电子科技, 2020, 33(4): 18-22.
[14]	汪欣,吴薇,曾照. 基于视频的人脸检测算法研究[J]. 电子科技, 2020, 33(2): 25-31.
[15]	王正军,姚一鸣,陈龙. 基于卷积神经网络的PCB板通用分拣算法[J]. 电子科技, 2020, 33(2): 60-65.