新闻视频显著人脸检索系统

该文针对新闻视频设计并实现了一个显著人脸检索系统。首先将新闻视频分割成镜头序列,利用训练好的CascadeAdaboost人脸检测器对每个镜头检测出一定数目的候选人脸,按照一些规则选取可信度高的作为样本,用于提取该镜头内的肤色模型。接着对肤色分割后的区域进行位置、大小分析和模板匹配,以淘汰非人脸区域,确定待跟踪的对象列表。为了做精确的跟踪和识别,系统对每个跟踪对象建立更细致的肤色模型。跟踪过程中每间隔一定帧数重新进行人脸检测,以减少误差积累和探测是否有新人脸出现。最后从每个人脸序列挑选最适合进行人脸识别的图像建立其特征脸空间,结合肤色信息和PCA算法判断其是否为要检索的目标人脸。     

快速人脸检测系统的设计与实现

为了快速而准确地在复杂背景的灰度或彩色图像中检测人脸,对实时人脸检测方法做了一些扩展并根据该方法设计实现了一个快速人脸检测系统,系统分为训练和检测两部分,训练方法为AdaBoost算法。测试表明该系统的性能较好。     

基于多特征融合的轻量化无锚人脸检测方法

针对大多数基于深度卷积网络的人脸检测方法存在因模型参数和计算量大造成的检测速度慢,以及复杂场景下人脸检测准确率低的问题,提出一种基于多特征融合的轻量化无锚人脸检测方法。构造轻量化卷积神经网络作为特征提取的骨干网络,以加速网络计算过程;引入三种模块处理并融合特征层,包括：感受野增强模块强化图片信息提取、权重特征融合模块提升检测准确性以及通道混洗融合模块简化计算过程;使用中心点定位的无锚检测方法对融合后的特征进行预测。实验结果显示,该方法模型参数量仅为5.1?MB,对比该基准方法,在WIDERFACE验证集中的简单、中等和困难难度的检测准确率分别提升1.4、2.2和4.8个百分点,表明该方法在保证模型轻量化的同时对复杂场景人脸有着较高的检测精度,验证了所提方法的有效性。     

基于C＋＋和OpenCV的人脸识别系统的设计与实现

人脸识别是通过对人的脸部特征信息进行身份识别的一种生物识别技术.研究现实基于人脸识别的身份验证系统具有重要意义.提出一种基于C＋＋和OpenCV的人脸识别系统的设计与实现方法.系统实现的功能模块包括人脸图像采集、图像预处理、人脸检测与定位、人脸特征提取与识别、人脸数据库管理等.系统中人脸检测采用基于Haar-like特征的Haar 分类器,人脸识别采用Eigenfacae算法.系统经过运行测试,结果表明该系统能够满足一般的基于人脸识别的身份验证需求.     

视频下的正面人体身份自动识别

为了能够实现视频下正面人体身份的自动识别,设计的系统包括Adaboost行人检测、Adaboost人脸检测、肤色验证、步态预处理、周期检测、特征提取以及决策级融合识别等模块.通过行人检测模块可以自动开启人脸检测模块和步态周期检测模块.实验结果表明,提出的根据下臂摇摆区域确定步态周期的方法对正面步态周期检测准确,计算量小,适用于实时的步态识别.采用人脸特征辅助步态特征在决策级的融合方法是解决视频下身份识别的新思路,在单样本的步态识别中,融合人脸特征可以提高识别精度.     

基于深度学习的重叠人脸检测

人脸检测在人机界面、安全系统、人脸识别、基于内容的图像检索等不同应用中起着重要作用。随着计算机图像技术的发展,人脸检测的方法也越来越多。但是利用现有的人脸检测方法检测重叠人脸时,虽然能够检测出部分人脸,但是相比于单人脸的检测,算法的效率和准确性都有所欠缺。针对这个问题,提出了一种基于深度学习的重叠人脸检测方法。首先基于机器学习方法,构建出多个人脸特征分类器,然后再利用肤色检测的方法对分类器得到的候选人脸进行二次检测,最后利用提出的一种NMS算法对候选人脸进行进一步的处理,从而检测出精确的人脸。为了验证算法的高效性和准确性,进行了多个人脸检测算法的对比实验,结果表明,该算法在效率和准确性方面都有较大提高。     

一种基于视频流的快速人脸检测算法

人脸检测广泛用于计算机视觉和模式识别领域。结合肤色检测和镶嵌图方法,提出一种对视频流中人脸进行快速检测的算法。该方法首先根据肤色信息和人脸的几何规则初步得到可能的人脸区,然后在候选区中利用改进的镶嵌图方法准确定位人脸。实验表明,该方法能快速而且准确地在视频流中进行人脸检测。     

Face detection and recognition of natural human emotion using Markov random fields

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research.     

Enhanced long-range personal identification based on multimodal information of human features

This work presents an enhanced long-range personal identification scheme using multimodal information of human features. Multimodal information includes multiview face detection, height measurement and face recognition. Multiview faces are estimated by collecting five face databases that correspond to left, half-left, right, half-right, and frontal faces, respectively. The sequences of parameters based on four detectors are also designed to determine the face direction. The detectors use the head-shoulder region, frontal face, profile face, and eyes detector respectively. In addition to determining when individuals enter the monitoring area, the multiview face detection module also describes the detected face direction. This result allows the identification system to select the face database of a specific direction to identify subsequent faces. Additionally, the height measurement module estimates individual height by calculating the vanishing points and lines. The module concept is based on single-view metrology. The measured information further refines the face database selected by multiview face detection and minimizes the candidates for face identification. Importantly, the proposed method integrates the multimodal information based on face direction, height and face features to refine the database and analyzes the information to determine the identity of a person. In this work, images from a monitoring area 5.6 m away from a camera are captured using an inexpensive digital web camera. The experimental results show that the proposed method can improve the accuracy rate by more than 21 % in contrast with the baselines and correspondingly demonstrates the effectiveness of the proposed idea.     

基于肤色模型和FloatBoost的多姿态人脸检测

多姿态人脸检测是人脸识别系统必须解决的关键问题之一。利用光照鲁棒的肤色模型来搜索待检图像的可能人脸区域并进行肤色分割,结合分割区域的几何信息确定最终的候选人脸区域,然后对人脸的关键特征进行定位,按规则计算重要特征块的中心,将这些中心点确定的符合条件的候选区域利用FloatBoost进行分类,最终实现了快速准确的多姿态人脸检测。     

An Expression Deformation Approach to Non-rigid 3D Face Recognition

The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively.     

基于在线特征选择的实时多姿态人脸跟踪

针对复杂条件下的人脸跟踪问题, 将显著区域跟踪算法和基于 Adaboost 的人脸检测算法相结合, 研发了一个实时多姿态人脸跟踪系统. 系统采用数据关联结果, 自动选择和切换检测器与跟踪器, 并通过引入环境信息增强跟踪算法的稳定性. 实验表明, 系统可在目标姿态变化、摄像机运动等复杂条件下进行自动人脸检测与跟踪, 对 320x240 的图像序列处理速度达到 10-12帧/秒.     

A statistic approach to the detection of human faces in color nature scene

In this paper, a novel algorithm for oriental face detection is presented to locate multiple faces in color scenery images. A binary skin color map is first obtained by applying the skin/non-skin color classification algorithm. Then, color regions corresponding to the facial and non-facial areas in the color map are separated with a clustering-based splitting algorithm. Thereafter, an elliptic face model is devised to crop the real human faces through the shape location procedure. Last, local thresholding technique and a statistic-based verification procedure are utilized to confirm the human faces. The proposed detection algorithm combines both the color and shape properties of faces. In this work, the color span of human face can be expanded as wilder as possible to cover different faces by using the clustering-based splitting algorithm. Experimental results reveal the feasibility of our proposed approach in solving face detection problem.     

基于在线特征选择的实时多姿态人脸跟踪

This paper presents a system that is able to reliably track multiple faces under varying poses(tilted and rotated)in real time.The system consists of two interactive modules.The first module performs the detection of the face that is subject to rotation. The second module carries out online learning-based face tracking.A mechanism that switches between the two modules is embedded into the system to automatically decide the best strategy for reliable tracking.The mechanism enables a smooth transit between the detection and tracking modules when one of them gives either nil or unreliable results.Extensive experiments demonstrate that the system can reliably carry out real time tracking of multiple faces in a complex background under different conditions such as out-of-plane rotation,tilting,fast nonlinear motion,partial occlusion,large scale changes,and camera motion.Moreover,it runs at a high speed of 10～12 frames per second(fps)for an image of 320×240.     

Emulating biological strategies for uncontrolled face recognition

Face recognition technology is of great significance for applications involving national security and crime prevention. Despite enormous progress in this field, machine-based system is still far from the goal of matching the versatility and reliability of human face recognition. In this paper, we show that a simple system designed by emulating biological strategies of human visual system can largely surpass the state-of-the-art performance on uncontrolled face recognition. In particular, the proposed system integrates dual retinal texture and color features for face representation, an incremental robust discriminant model for high level face coding, and a hierarchical cue-fusion method for similarity qualification. We demonstrate the strength of the system on the large-scale face verification task following the evaluation protocol of the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4. The results are surprisingly well: Its modules significantly outperform their state-of-the-art counterparts, such as Gabor image representation, local binary patterns, and enhanced Fisher linear discriminant model. Furthermore, applying the integrated system to the FRGC version 2 Experiment 4, the verification rate at the false acceptance rate of 0.1 percent reaches to 93.12 percent.     

双正交小波方法在面部特征抽取中的应用

人脸识别技术是生物鉴别技术的重要组成部分。脸部特征抽取是人脸识别技术的关键。首先对基于小波极大模的边缘检测算法进行改进 ,提出极大模区域边缘检测算法 ;然后提出一种人脸特征抽取算法。整个脸部特征抽取过程分为三部分 :1 )对图像进行二维小波分解 ;2 )背景分离 ,脸部目标定位 ;3 )脸部特征抽取。实验证明该算法可以准确地抽取人脸特征     

复杂背景基于肤色的人脸检测

本文针对复杂背景的彩色静止图像的人脸检测提出了一种基于肤色检测和分块面部特验证方法,。先在类肤色区域内提取出面部特征,然后用分块验证的方法来确定人脸。本算法可以快速检测不同大小,不同平面及一定侧面旋转角度的人脸,而且可以适应一定程度的表情变化。     

基于肤色和改进的贝叶斯分类器的人脸检测

提出了一种新的基于肤色的多人脸检测方法.该方法先通过肤色分割得到人脸候选区,然后结合图像的小波表示和主元分析方法通过训练得到可用于区分人脸和非人脸的特征向量,并用改进的贝叶斯分类器对输入图像进行多人脸检测,改进的判决准则中参数ω,可用于控制检测的准确率和虚警概率,通过设定不同ω值可使算法适用于不同要求的应用,另外为保证获得较高准确率的同时降低虚警概率,还提出在经分类器判决后的人脸区域中依据对应的马赛克模板进一步排除虚假人脸.     

一种低成本嵌入式汽车安保系统

本文提出了一种低成本可扩展的嵌入式汽车安保监控系统,该系统由一个人脸检测模块,一个GPS定位子系统、一个GSM模块以及一个基于ARM内核的控制平台构成。人脸检测模块基于改进的AdaBoost算法实现,用于实时监测车内有无人员出现。实验结果证明了本文的汽车安保监控系统的有效性,并比目前的其他传统的汽车安保系统更"智能化"。     

基于EHMM的人脸识别算法

人脸识别是对从视频图像中检测到的人脸区域进行身份的认证．是将待识别人脸与数据库中的人脸进行匹配的过程。将EHMM应用于人脸识别,提取人脸的DCT系数特征作为观察向量,用EHMM算法进行人脸模型训练和识别,并使用OpenCV对人脸识别算法进行功能仿真验证和相关探究,达到较好的人脸识别效果。实验结果表明,正常光照下,该算法的识别率在95％以上。