Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.

基于AdaBoost学习策略的污水近红外光谱快速检测

对实际污水样本的近红外光谱数据进行建模,可以预测水质指标,实现污水水质监测。但实际污水样本的多样性不足,标签值集中在某个较低的区间内,样本间离散度低、区分度小,导致近红外光谱数据和标签值间的相关性较弱,一般的分类模型和回归模型的预测准确度较低。因此,利用自适应增强(adaptive boosting,AdaBoost)算法进行建模以提高模型的准确度,利用集成策略将多个子学习器组合为一个准确度更高的强学习器。此外,人为配置具有浓度梯度的标准样本对实际污水样本进行补充,以减弱实际污水样本的多样性不足对建模精度的影响。在不同数据集上对AdaBoost算法和其他常用算法进行了对比,对比结果证明了AdaBoost算法在污水水质快速检测方面的有效性。

基于Gentle Adaboost的气密性检测系统

差压法气密性检测易受外部因素与预设参数影响。针对问题基于集成学习建立气密性检测系统,包含传感器终端数据采集系统、人机交互界面,并用最小二乘法对传感器进行线性拟合,利用Gentle Adaboost算法寻找每轮迭代中最佳弱分类器并更新下一轮样本权重,通过集成数轮迭代中最佳弱分类器组成强分类器,对被测物的气密性能进行判断。实验结果表明:所提系统在气密性检测中的准确度、精确度与召回率皆优于传统方法与单一分类模型,准确度达到99.8%,能有效克服外部因素对检测结果的影响,提高了差压法气密性检测的准确性与稳定性。

基于AdaBoost和AAM的面部特征点检测技术研究

文章报告了面部特征点检测的现状,分析了AdaBoost算法的分类性能和AAM模型的建模特性。对面部特征点检测进行了研究,通过训练多个弱分类器并组合它们,提高了面部特征点检测的准确性和鲁棒性。利用AdaBoost强分类器识别的结果作为AAM模型训练的输入,提取面部特征点候选区域,降低了AAM模型重构次数,进一步降低了计算复杂度,尤其是在面部姿态和表情变化较大的情况下,提高了匹配的准确率。同时,AAM模型可以为AdaBoost提供一个更为精细的面部特征点定位,从而提高整体的面部特征点检测性能。

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

Real-Time Multi-View Face Detection and Pose Estimation Based on Cost-Sensitive AdaBoost

Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.

基于AdaBoost算法的新能源汽车电机异常故障检测

新能源汽车的电机系统包含许多复杂的部件和子系统,部件之间的相互作用使得异常故障的检测变得复杂,而电机异常故障检测主要采用人工检测方式,即通过耳朵听声音,用眼睛观察,用手触摸找出故障位置,导致故障检测精度较低。因此,提出AdaBoost算法下新能源汽车电机异常故障检测方法。通过传感器采集电机信号,采用距离相似度、模糊隶属度函数提取信号特征,借助遗传算法的编码操作、交叉操作及其变异操作获取关键信号特征,运用自适应增强(Adaptive Boosting,AdaBoost)算法将信号特征分成正常信号和异常故障,以此实现对新能源汽车电机异常故障检测。实验结果表明,所提算法电机异常故障检测精度高,且耗时短。

Face Detection under Complex Background and Illumination

For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.

基于Adaboost算法的人脸图像情绪识别

提出一种基于Adaboost算法对人脸图像进行情绪识别的方法。先将视频进行图像数据采集,再通过基于Haar特征值的自适应增强计算,即Adaboost计算检测人脸特征,将迁移机器学习技术运用到多任务的卷积式神经网络,然后利用卷积神经网络的情绪回归计算人脸表情的效价和唤醒度得分。不但可以解决对复杂背景图像的高误检率问题,而且还可以解决对多姿态单人脸图像的低检率问题。经仿真试验证明,该方法对单人脸、多人脸和复杂背景多人图像都有较好的测量效果,实用性较强。

基于AdaBoost的人脸检测算法

着眼于图像识别技术的研究与探索,以MATLAB仿真软件为算法验证平台,采用AdaBoost算法进行人脸检测的研究。主要过程是利用Haar-like模型来表征人脸器官,在仿真软件中通过“积分图”转换来寻求Haar-like特征数值的快速计算。利用AdaBoost算法训练出一些具备人脸特性的弱分类器,通过设置不同权重的方式,把性能最佳的弱分类器群打造成为强分类器。实验结果表明,该算法能够实现人脸的快速有效检测,在智能安防系统中有广泛应用价值。

Multi-angle Face Detection Based on DP-Adaboost

Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and hairstyle.This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate.An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face.An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm.Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier,the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.

Region Pair Grey Difference Classifier for Face Detection

A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.

Secure Rotation Invariant Face Detection System for Authentication

Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

一种可用于鉴别肝癌呼气信号的改进AdaBoost算法

提出一种改进的AdaBoost强化学习算法,并将其应用于鉴别健康者和肝癌患者的呼气信号。首先采集志愿者(包括健康对照组和肝癌患者)的呼气信号,利用Relief算法提取其主要特征;接着融合Stacking模型,基于传统的机器学习算法训练得到若干基分类器组,构建一个个子分类器。为减少训练样本对分类器性能的影响,利用K折交叉,先后得到k个基分类器,形成一个基分类器组;进一步,由投票法得到该基分类器组,即子分类器对测试集的预测结果;然后根据各子分类器对训练集的预测错误率调整训练样本,并获得各子分类器的权重系数;最后将多个子分类器的预测结果进行加权组合,得到最终预测结果。实验结果表明,相比传统的AdaBoost算法,改进的AdaBoost算法在鉴别肝癌呼气和健康对照组呼气时,错误率明显下降,鲁棒性有所提升。该算法在鉴别肝癌呼气时,准确率可以达到90%左右,特异性和精确度也均超过95%。因此,改进的AdaBoost算法可有效提升肝癌呼气鉴别精度,对通过呼气鉴别肝癌、实现早期诊断的研究具有重要意义。

Efficient Approach for Face Detection in Video Surveillance

Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presented to detect faces in video surveillance.Firstly,both the skin-color and motion components are applied to extract skin-like regions.The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm.Secondly,the image is clustered into separated face candidates by using the region growing technique.Finally,the face candidates are further verified by the rule-based algorithm.Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.

Face Detection from Four Captured Images Related to Intelligent Room for the Deaf

The intelligent environment needs Human-Computer Interactive technology （HCI） and a projector projects screen on wall in the intelligent environments. We propose the front-face detection from four captured images related to the intelligent room for the deaf. Our proposal purpose is that a deaf user faces wall displaying everywhere. system gets the images from four cameras, and detects the user region from a silhouette image using a different method, detects and cuts a motion body region from a different image, and cuts the vertexchest region from the cut body region image. The system attempts to find front-face using Haar-like feature, and selects a detected front-face image from the vertex-chest region. We estimate the front-face detection of recognition rate, which shows somewhat successfully.

An Integrated Intrusion Detection System by Combining SVM with AdaBoost

In the Internet, computers and network equipments are threatened by malicious intrusion, which seriously affects the security of the network. Intrusion behavior has the characteristics of fast upgrade, strong concealment and randomness, so that traditional methods of intrusion detection?system (IDS) are difficult to prevent the attacks effectively. In this paper, an integrated network?intrusion detection algorithm by combining support vector machine (SVM) with AdaBoost was?presented. The SVM is used to construct base classifiers, and the AdaBoost is used for training?these learning modules and generating the final intrusion detection model by iterating to update the weight of samples and detection model, until the number of iterations or the accuracy of detection model achieves target setting. The effectiveness of the proposed IDS is evaluated using?DARPA99 datasets. Accuracy, a criterion, is used to evaluate the detection performance of the proposed IDS. Experimental results show that it achieves better performance when compared?with two state-of-the-art IDS.

基于Adaboost与LDP算法的人脸识别研究

针对人脸识别过程中识别时间和成功率等问题,提出一种基于改进Adaboost与LDP算法的人脸识别算法。对于采集到的图像首先使用基于YCbcr模型的皮肤分割算法,去除图像中的非人脸部分,减少图像的大小,节约特征提取的时间。再使用基于Adaboost的级联分类器检测出人脸区域。最后利用改进后的局部方向模式提取人脸特征进行人脸识别。使用yale人脸库作为样本集进行了实验,实验结果表明:结合了肤色分割的Adaboost算法检测成功率增加、耗时减少,改进后的LDP算法与传统LDP用时相差不大,但是识别成功率有所提高。

Fast Face Detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features

Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.

AdaBoost结合改进高斯混合模型的人体检测算法

室内视频监控的客流量统计场景由于背景光照变化、人群拥挤等因素影响,导致背景更新、目标提取和识别的准确率较低,同时由于算法的实时性满足不了高帧率视频(60 fps)的要求,使得识别统计的准确率低于95%。针对以上问题设计室内人体检测识别算法,首先通过将运行期均值法与高斯混合背景建模相结合,根据像素值进行去重合并,以减少相似像素重复计算,并将噪音点在一定范围内采用均值法,进一步从实时性上提高背景提取效果;其次通过自适应阈值法,根据区域光照强度变化,自适应调节分割阈值,从而避免光照不均而影响检测结果;识别采用一种基于AdaBoost的人体头肩定位与最短距离分类器相结合的方法对人体进行识别,根据运动物体的实际位置,对人体头肩进行初步定位,然后提取关于人体头部的特征量:圆形度、肩宽比等,最后通过结合最短距离分类器,对人体进行分类识别。在高帧率视频实验中对复杂多人的每帧图片的处理耗时基本在15 ms以内,人体识别准确率达到98%。实验证明方法能够解决复杂变化背景与多人场景下的高帧率视频多目标人体检测、识别与统计。