共查询到18条相似文献,搜索用时 156 毫秒
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人脸自动定位技术在智能视频通信、视频监控以及娱乐等领域有着广泛的应用。通过将基于肤色的人脸检测和基于人工神经网络的控制策略相结合,提出一种新的人脸自动定位算法。该算法简单有效,克服传统跟踪算法中需要利用帧间相关信息和需要标定摄像机的缺点,只需通过人脸检测程序给出人脸特征点在计算机图像中的坐标,就可直接得出摄像机水平调整量和垂直调整量,根据调整量控制摄像机运动即可将人脸自动定位在图像中心。最后利用面向对象的方法实现了系统,并且取得了满意的效果。 相似文献
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将基于Boosted Cascade的人脸检测算法运用到视频图像当中,并结合图像序列中的运动信息,提出并实现了一种实时的人脸检测跟踪系统.首先根据图像的运动信息提取出可能存在人脸的候选区域,然后在候选区域中用Boosted Cascade算法进行检测.实验结果表明该系统能够实时地对于人脸进行检测跟踪,可以被应用在智能视频监控方面. 相似文献
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基于YCbCr高斯肤色模型的人脸检测技术研究 总被引:2,自引:0,他引:2
人脸的检测是人脸信息处理领域中的一项关键技术,其自动人脸识别系统、基于内容的检索、视觉监测、新一代人机交互技术等领域具有广阔的应用前景。而高斯模型是模型识别研究中经常用到的一个模型。建立一种基于YCbCr高斯肤色模型,并对其进行验证和分析,随后对二值化图像进行基于数学形态学和连通区域分析和处理,尽可能地去掉大部分非人脸区域,对于人脸识别的研究有一定的价值和意义。 相似文献
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实现的人脸检测跟踪与特征点定位系统,基于VC++6.0开发平台,使用opencv作为开发工具,有效缩短了系统的开发时间。首先,本系统采用adaboost算法进行人脸检测,通过合理的特征模板的选择实现了人脸的实时检测;其次,人脸跟踪模块选用camshift算法,利用人脸检测模块生成的人脸坐标传递给跟踪模块,实现人脸的自动实时跟踪,同时建立多个camshift跟踪器对多人脸进行跟踪,并有效地解决了人脸遮挡的问题;最后,通过ASM(active shapemodel)算法实现了实时人脸特征点定位。实验结果表明该系统实现的人脸实时检测跟踪及特征点定位,效果明显,可以作为表情分析和情感计算、视频人脸识别开发的基础。 相似文献
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陈海峰 《微电子学与计算机》2010,27(2)
人脸表情识别和软件换肤分别作为人机交互中的智能化和人性化技术得到了深入的研究.文中结合以上两种技术提出了基于人脸表情识别的软件界面自动换肤思想,介绍了一种软件界面自动换肤系统的设计,并讨论了主成分分析检测定位模型.仿真结果证明了该方案的可行性. 相似文献
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人脸识别技术目前是生物特征识别中研究的热点之一,在商业、司法、监控和视频检索等领域有着广泛的应用前景。人脸特征的自动提取则是人脸自动识别过程中至关重要的环节。主要基于主成分分析PCA、线性差别分析LDA和非负矩阵分析NMF三种常用的子空间分析方法进行人脸特征提取的研究。 相似文献
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Zhenqiu Zhang Gerasimos Potamianos Andrew W. Senior Thomas S. Huang 《Signal, Image and Video Processing》2007,1(2):163-178
The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location
information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system
is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart
room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional
(2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance
model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed
in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple
tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal
dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an
adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift
and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the
lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves
excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative
tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in
the available frame views.
This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the
IBM T.J. Watson Research Center. 相似文献
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Wenlong Zheng Suchendra M. Bhandarkar 《Journal of Visual Communication and Image Representation》2009,20(1):9-27
A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (APF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate estimates of the proposal distribution and the posterior distribution than the standard Particle Filter thus enabling more accurate tracking in video sequences. In the proposed BAPF algorithm, the AdaBoost algorithm is used to detect faces in input image frames, whereas the APF algorithm is designed to track faces in video sequences. The proposed BAPF algorithm is employed for face detection, face verification, and face tracking in video sequences. Experimental results show that the proposed BAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios. 相似文献
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《Signal Processing: Image Communication》2002,17(2):145-164
Automatic semantic video object extraction is an important step for providing content-based video coding, indexing and retrieval. However, it is very difficult to design a generic semantic video object extraction technique, which can provide variant semantic video objects by using the same function. Since the presence and absence of persons in an image sequence provide important clues about video content, automatic face detection and human being generation are very attractive for content-based video database applications. For this reason, we propose a novel face detection and semantic human object generation algorithm. The homogeneous image regions with accurate boundaries are first obtained by integrating the results of color edge detection and region growing procedures. The human faces are detected from these homogeneous image regions by using skin color segmentation and facial filters. These detected faces are then used as object seed for semantic human object generation. The correspondences of the detected faces and semantic human objects along time axis are further exploited by a contour-based temporal tracking procedure. 相似文献
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如何有效利用视频中人脸之间的时空连续性信息来克服人脸分辨率低、图像尺度变化大和姿态、光照变化以及遮挡等问题是视频人脸识别的关键所在.提出了一种基于流形学习的视频人脸性别识别算法.该算法不仅可以通过聚类融合学习来挖掘视频内在的连续性信息,同时能发现人脸数据中内在非线性结构信息而获得低维本质的流形结构.在UCSD/Honda和自采集数据库上与静态的算法比较结果表明,所提算法能够获得更好的识别率. 相似文献
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提出采用低功耗芯片EP3C40为核心,结合Nios II软核处理器的高处理性能,实现人脸视频信号采集与跟踪,并将相关检测跟踪算法移植于Nios II处理器协同工作的方式实现整体系统的工作。实验结果表明,在环境微弱变化以及人脸不同姿态下,系统都能检测并准确跟踪,同时硬件可重构能力强,算法简单、快速、鲁棒性强。 相似文献
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文中实现了彩色序列图像的人脸检测和识别的系统.所用到的关键性技术是肤色运动分析、主成分分析(PCA)和支撑向量机(SVM).首先根据彩色序列图像中人脸的色度特性、运动特性、几何特性和灰度分布特性完成人脸的检测工作;其次通过人脸模式之间的相关性进行主成分分析,提取并且选择特征,将所选择的特征训练SVM,最后用已经训练好的SVM完成对人脸的识别任务.系统算法结构遵循以下原则:先使用运算量少的简单方法尽可能减少搜索空间,然后在已经大大减少的空间中再用复杂方法处理,可以在保持高的检测和识别率的同时,提高系统响应速度. 相似文献
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为了从大字典视频中跟踪和识别人脸,提出了一种基于平均序列的稀疏表示分类端到端识别方法。首先,利用所有可用视频数据和属于同一个人的人脸跟踪帧进行联合优化;然后,将严格的时间约束添加到l1-最小化;最后,运用人脸跟踪中所有单个帧重建各个身份,利用稀疏重建完成人脸分类。在You Tube人脸数据集上的实验验证了本文方法的有效性,在You Tube名人数据集和本文搜集的电影预告片数据集上的实验结果表明,相比几种较为新颖的分类方法,该方法取得了更高的识别精度,并且在拒绝不明身份上的准确率比SVM高8%。 相似文献