Haar-like特征双阈值Adaboost人脸检测

目的 针对基于Haar-like特征的Adaboost人脸检测算法,在应用于视频流时训练的时间较长,以及检测效率较低的问题,提出了一种基于区间阈值的Adaboost人脸检测算法。方法 通过运行传统的Adaboost算法对人脸图像Haar-like特征值进行提取分析后,对人脸样本与非人脸样本特征值进行比较,发现在某一特定的特征值区间内,人脸和非人脸区域能够得到准确区分,根据此特性,进行分类器的选择,在简化弱分类器计算步骤的同时,降低训练时间,提高对人脸的识别能力。除此之外,弱分类器的增强通过Adaboost算法的放大使得强分类器分类精度提高,与级联结构的配合使用也提升了最终模型检测人脸的准确率。结果 利用MIT（Massachusetts Institute of Technology）标准人脸库对改进Adaboost算法的性能进行验证,通过实验验证结果可知,改进后的Adaboost人脸检测算法训练速度提升为原来的1.44倍,检测率上升到94.93%,虚警率下降到6.03%。并且将改进算法在ORL（Olivetti Research Laboratory）、FERET（face recognition technology）以及CMU Multi-PIE（the CMU Multi-PIE face database）这3种标准人脸库中,分别与SVM（support vector machine）、DL（deep learning）、CNN（convolutional neural networks）以及肤色模型等4种算法进行了人脸检测对比实验,实验结果显示,改进后的Adaboost算法在进行人脸检测时,检测率提升了2.66%,训练所需时间减少至624.45 s,检测效果明显提升。结论 提出的基于区间阈值的Adaboost人脸检测算法,在分类器的训练和人脸检测方面都比传统的Adaboost算法性能更高,能够更好地满足人员较密集处（如球场等地）对多人脸同时检测的实际需求。     

动态权值预划分实值Adaboost人脸检测算法

提出了Real-Adaboost的一种改进算法。该算法采用预先计算类Haar特征所对应弱分类器在样本空间的划分，并动态更新人脸训练样本的权值。与以往的Real-Adaboost算法比较，该算法大大缩短了训练时间，算法训练时间复杂度降到O(T*M*N)，同时加速了强分类器的收敛性能，减少检测器的弱分类器数量，减少检测时间。     

Boosted cascade of scattered rectangle features for object detection

This paper presents a variant of Haar-like feature used in Viola and Jones detection framework,called scattered rectangle feature,based on the common-component analysis of local region feature. Three common components,feature filter,feature structure and feature form,are extracted without concern-ing the details of the studied region features,which cast a new light on region feature design for spe-cific applications and requirements: modifying some component(s) of a feature for an improved one or combining different components of existing features for a new favorable one. Scattered rectangle feature follows the former way,extending the feature structure component of Haar-like feature out of the restriction of the geometry adjacency rule,which results in a richer representation that explores much more orientations other than horizontal,vertical and diagonal,as well as misaligned,detached and non-rectangle shape information that is unreachable to Haar-like feature. The training result of the two face detectors in the experiments illustrates the benefits of scattered rectangle feature empirically; the comparison of the ROC curves under a rigid and objective detection criterion on MIT CMU upright face test set shows that the cascade based on scattered rectangle features outperforms that based on Haar-like features.     

多特征融合的道路车辆检测方法

通过改进基于Haar-like特征和Adaboost的级联分类器,提出一种融合Haar-like特征和HOG特征的道路车辆检测方法。在传统级联分类器的Harr-like特征基础上引入HOG特征;为Haar-like特征和HOG特征分别设计不同形式的弱分类器,对每一个特征进行弱分类器的训练,用Gentle Adaboost算法代替Discrete Adaboost算法进行强分类器的训练;在级联分类器的最后几层上使用Adaboost算法挑选出来的特征组成特征向量训练SVM分类器。实验结果表明所提出的方法能有效检测道路车辆。     

基于Adaboost算法的多角度人脸检测

文中提出了一种基于Adaboost算法的多角度人脸检测方法.多角度人脸检测问题的研究与正面人脸检测相比,相对薄弱,离实际应用的需求还比较远.首先使用Haar特征设计并构造弱分类器空间,用Adaboost算法学习得到基于视图的多分类器级联的人脸检测器;然后将多角度人脸划分成三类:全侧脸,半侧脸及正面人脸,并为不同角度的人脸建立不同的检测器分别用于检测.在CMU侧面人脸检测集合上,用基于Adaboost的方法对多角度人脸图像进行仿真实验,检测正确率为89.8%,误报数为243个.相比Schneiderman等人的方法,该方法具有更好的性能.     

多通道Haar-like特征多示例学习目标跟踪

目的 提出一种基于多通道Haar-like特征的多示例学习目标跟踪算法,克服了多示例跟踪算法在处理彩色视频时利用信息少和弱特征不能更换的缺点。方法 首先,针对原始多示例学习跟踪算法对彩色视频帧采用单通道信息或将其简单转化为灰度图像进行跟踪会丢失部分特征信息的缺点,提出在RGB三通道上生成位置、大小和通道完全随机的Haar-like特征来更好地表示目标。其次,针对多示例学习跟踪算法中Haar-like弱特征不能更换,难以反映目标自身和外界条件变化的特点,提出在弱分类器选择过程中,用随机生成的新Haar-like特征实时替换部分判别力最弱的Haar-like特征,从而在目标模型中引入新的信息,以适应目标外观的动态变化。结果 对8个具有挑战性的彩色视频序列的实验结果表明,与原始多示例学习跟踪算法、加权多示例学习跟踪算法、基于分布场的跟踪算法相比,提出的方法不仅获得了最小的平均中心误差,而且平均跟踪准确率比上述3种算法分别高52.85%,34.75%和5.71%,在4种算法中获得最优性能。结论 通过将Haar-like特征从RGB三通道随机生成,并将判别力最弱的部分Haar-like弱特征实时更换,显著提升了原始多示例学习跟踪算法对彩色视频的跟踪效果,扩展了其应用前景。     

基于模糊集理论的人脸检测

分析了模糊集理论运用于人脸检测的可行性,采用Haar矩形特征和隶属度函数对样本集进行训练,运用特征集的熵和AdaBoost算法选取适当的弱分类器,并构建了分发型人脸检测器。检测时,对于不像人脸的子窗口通过靠前的结构简单的强分类器快速将其淘汰掉;对于像人脸的子窗口,根据其与人脸的相似程度,由分发器动态地选择后面的强分类器进行判定。在MIT+CMU的正面人脸图片集中进行了测试,实验结果表明,此检测器在检测性能降低不大的情况下,可以有效地提高检测效率。     

基于新Haar-like特征的多角度人脸检测

在Haar-like特征的基础上增加新的检测特征,给出特征计算方法和积分方法,实现多角度人脸检测。将多角度人脸分为3类,即全侧脸、半侧脸和正面人脸。利用连续Adaboost算法训练各类人脸检测器,用金字塔式结构将各类人脸检测器级联成一个多角度人脸检测器。在CMU人脸检测集合上,该检测器的成功率为85．2％,高于Adaboost算法和浮点Adaboost算法。     

Haar-like features with optimally weighted rectangles for rapid object detection

This article proposes an extension of Haar-like features for their use in rapid object detection systems. These features differ from the traditional ones in that their rectangles are assigned optimal weights so as to maximize their ability to discriminate objects from clutter (non-objects). These features maintain the simplicity of evaluation of the traditional formulation while being more discriminative. The proposed features were trained to detect two types of objects: human frontal faces and human heart regions. Our experimental results suggest that the object detectors based on the proposed features are more accurate and faster than the object detectors built with traditional Haar-like features.     

Hand gesture recognition using two-level speed normalization,feature selection and classifier fusion

Hand gesture recognition provides an alternative way to many devices for human computer interaction. In this work, we have developed a classifier fusion based dynamic free-air hand gesture recognition system to identify the isolated gestures. Different users gesticulate at different speed for the same gesture. Hence, when comparing different samples of the same gesture, variations due to difference in gesturing speed should not contribute to the dissimilarity score. Thus, we have introduced a two-level speed normalization procedure using DTW and Euclidean distance-based techniques. Three features such as ‘orientation between consecutive points’, ‘speed’ and ‘orientation between first and every trajectory points’ were used for the speed normalization. Moreover, in feature extraction stage, 44 features were selected from the existing literatures. Use of total feature set could lead to overfitting, information redundancy and may increase the computational complexity due to higher dimension. Thus, we have tried to overcome this difficulty by selecting optimal set of features using analysis of variance and incremental feature selection techniques. The performance of the system was evaluated using this optimal set of features for different individual classifiers such as ANN, SVM, k-NN and Naïve Bayes. Finally, the decisions of the individual classifiers were combined using classifier fusion model. Based on the experimental results it may be concluded that classifier fusion provides satisfactory results compared to other individual classifiers. An accuracy of 94.78 % was achieved using the classifier fusion technique as compared to baseline CRF (85.07 %) and HCRF (89.91 %) models.     

基于改进的Mean-shift驾驶员人脸跟踪算法研究

为了实现对驾驶员人脸实时跟踪,提出了一种改进的Mean-shift算法。首先对人脸提取类Haar特征,使用类Haar特征构造弱分类器,然后根据样本的权值分布构造出强分类器,形成人脸检测分类器;由于光照变化等因素的影响,引入红外主动照明模式,通过隔离可见光照,基本上消除了光照变化对人脸检测造成的影响;针对Mean-shift算法在被跟踪目标发生快速移动时容易跟踪失败的缺点,改进了Mean-shift算法：当目标发生快速移动时,采用SSD（Sum of Square Dif-ference）算法进行全局搜索。以实际驾驶员人脸检测与跟踪实验为例进行了大量实验,提出的方法比Mean-shift算法的速度快、准确度高。     

复杂背景下具有偏转角度的正面人脸检测

美国的Voila博士提出的基于Haar-like特征的人脸检测算法是一种具有巨大发展潜力的新算法,快速而准确。通过研究,认为这一算法还存在两个有待改进的地方：一是在检测率和误检率之间难以权衡,二是可检测人脸姿态受训练样本制约。对此提出了改进措施,设计了一个可以检测相对于垂直方向有±45°偏转的正面人脸的检测算法,与基于Haar-like特征的人脸检测算法相比,具有更好的鲁棒性和更低的误差率。     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

人脸表情的实时分类

提出一种基于连续Adaboost算法的人脸表情实时分类方法,使用Haar特征设计了具有连续致信度输出的查找表型弱分类器形式,构造出弱分类器空间,采用连续Adaboost算法学习出人脸表情分类器,实验结果表明：文中方法与支持向量机方法相比,对于人脸表情分类的正确率相当,而速度快近300倍,具有实时性和非常明显的应用价值。     

Fast and Robust Face Detection Using Evolutionary Pruning

Face detection task can be considered as a classifier training problem. Finding the parameters of the classifier model by using training data is a complex process. To solve such a complex problem, evolutionary algorithms can be employed in cascade structure of classifiers. This paper proposes evolutionary pruning to reduce the number of weak classifiers in AdaBoost-based cascade detector, while maintaining the detection accuracy. The computation time is proportional to the number of weak classifiers and, therefore, a reduction in the number of weak classifiers results in an increased detection speed. Three kinds of cascade structures are compared by the number of weak classifiers. The efficiency in computation time of the proposed cascade structure is shown experimentally. It is also compared with the state-of-the-art face detectors, and the results show that the proposed method outperforms the previous studies. A multiview face detector is constructed by incorporating the three face detectors: frontal, left profile, and right profile.     

多特征融合的实时人手跟踪算法

由于复杂背景、形变以及运动造成的模糊等因素, 导致在自然场景下的人手跟踪仍然是一个具有挑战性的问题. 本文中, 结合运动、颜色和Haar-like特征来构造一个具有鲁棒性的实时人手检测算法. 尽管不能运用于所有的情形, 但Haar-like特征成功地去除了类似肤色的运动背景区域. 利用三个特征构造三个弱分类器, 然后将其结合成一个强分类器. 如果一个分类器已经确定了人手的位置, 其他分类器将不会执行, 否则将会为下一个分类器提供一个可能的区域. 文中实现了提出的算法, 并且在几个具有挑战性的视频序列上进行了实验.     

Real-Time Facial Feature Tracking on a Mobile Device

This paper presents an implementation of the Active Appearance Model that is able to track a face on a mobile device in real-time. We achieve this performance by discarding an explicit texture model, using fixed-point arithmetic for much of the computation, applying a sequence of models with increasing complexity, and exploiting a sparse basis projection via Haar-like features. Our results show that the Haar-like feature basis achieves similar performance to more traditional approaches while being more suitable for a mobile device. Finally, we discuss mobile applications of the system such as face verification, teleconferencing and human-computer interaction.     

Context modeling for facial landmark detection based on Non-Adjacent Rectangle (NAR) Haar-like feature

Automatically locating facial landmarks in images is an important task in computer vision. This paper proposes a novel context modeling method for facial landmark detection, which integrates context constraints together with local texture model in the cascaded AdaBoost framework. The motivation of our method lies in the basic human psychology observation that not only the local texture information but also the global context information is used for human to locate facial landmarks in faces. Therefore, in our solution, a novel type of feature, called Non-Adjacent Rectangle (NAR) Haar-like feature, is proposed to characterize the co-occurrence between facial landmarks and its surroundings, i.e., the context information, in terms of low-level features. For the locating task, traditional Haar-like features (characterizing local texture information) and NAR Haar-like features (characterizing context constraints in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, the most discriminative feature set is selected automatically and used for facial landmark detection. To verify the effectiveness of the proposed method, we evaluate our facial landmark detection algorithm on BioID and Cohn-Kanade face databases. Experimental results convincingly show that the NAR Haar-like feature is effective to model the context and our proposed algorithm impressively outperforms the published state-of-the-art methods. In addition, the generalization capability of the NAR Haar-like feature is further validated by extended applications to face detection task on FDDB face database.     

复杂背景下的三级级联快速正面人脸检测算法

针对复杂背景下的正面人脸检测问题,提出一种三级级联快速正面人脸检测算法：第一级使用 HSV 肤色模型,通过分析最大肤色连通区,快速排除非人脸区域;第二级采用 Haar-like 特征结合 AdaBoost 算法定位人脸区域;最后提出基于局部特征点加权的改进主动形状模型(W-ASM)算法匹配人脸的特征点坐标,通过68个特征点位置判断当前人脸图像是否是正面人脸。实验结果表明,本算法能准确识别出垂直于图像旋转不超过±5°的正面人脸,每幅图像(640×480)平均用时仅52ms,满足实时性要求。