基于深度卷积网络的3D人脸重构算法

基于单张人脸图片的3D人脸模型重构,无论是在计算机图形领域还是可见光成像领域都是一个极具挑战性的研究方向,对于人脸识别、人脸成像、人脸动画等实际应用更是具有重要意义。针对目前算法复杂度较高、运算量较大且存在局部最优解和初始化不良等问题,本文提出了一种基于深度卷积神经网络的单张图片向3D人脸自动重构算法。该算法首先基于3D转换模型来提取2D人脸图像的密集信息,然后构建深度卷积神经网络架构、设计总体损失函数,直接学习2D人脸图像从像素到3D坐标的映射,从而实现了3D人脸模型的自动构建。算法对比与仿真实验表明,该算法在3D人脸重建上的归一化平均误差更低,且仅需一张2D人脸图像便可自动重构生成3D人脸模型。所生成的3D人脸模型鲁棒性好,重构准确,完整保留表情细节,并且对不同姿态的人脸也具有较好的重建效果,能够在三维空间中无死角自由呈现,将满足更多实际应用需求。     

一种基于人脸检测的虚拟眼镜试戴技术

为了满足人们在线选购眼镜时能够虚拟试镜的需求,提出了基于人脸检测的虚拟眼镜试戴技术。首先利用opencv中集成Haar-like特征、Adaboost算法的人脸检测技术检测出人脸的位置,然后根据"三庭五眼"的理论及几何特征实现眼睛与眼镜区域的定位,最后利用数字图像处理技术并依据人脸、眼睛的位置把眼镜图像融合到人脸眼镜区域,实现虚拟试镜的目的。实验证明,该技术对于存在表情变化、遮挡和姿态变化的静态图像与视频帧序列均能达到虚拟试镜效果。     

多结点样条插值曲面的图像放大方法探微

曲面曲线的拟合方法之一是多结点样条插值,多结点样条插值方法具有插值核函数、正则性、逼近精度的频域特性。利用混合型多结点样条插值曲面的图像放大方法构造一个分块混合型多结点样条插值曲面给数字图像的每一个色彩分量,通过试验得知,该方法可以提供高质量的图像放大。采用一种加速算法可以该方法的效率得到提高,在动画制作与数字漫游系统等该加速算法得到很好的应用效果。     

基于梯度向量流场的眼睛特征提取

根据人脸图像的边缘梯度图提出了一种新的基于梯度向量流场的眼睛特征提取方法。该算法改进原有的梯度向量流迭代方程求解梯度向量流场,以梯度向量流场中的汇点作为候选点,通过人脸器官的几何位置关系等方法评价候选点以定位眼球。该算法能较好地容忍一定的光照变化、人脸的小角度倾斜和旋转、闭眼和眼镜等干扰。在具有以上干扰的ORL人脸库的400幅图像上的实验证明,该算法具有较好的眼睛特征抽取能力。     

基于参数域映射及B样条插值的三维重构方法

利用单目CCD图像进行物体表面非接触测量的核心是基于单幅图像的三维重构技术,常采用由阴影恢复形状(SFS)的方法实现三维重构。当图像分辨率较低时,通过由阴影恢复形状的方法重构的三维表面模型其分辨力较差,无法满足实际要求。为此提出了一种基于参数域映射及B样条插值的三维重构方法。采用B样条插值技术对图像进行放大处理,通过像素的参数域映射减小图像的失真及高频信息的丢失,根据放大后图像的灰度信息重构物体的三维表面模型。实验表明,基于参数域映射及B样条技术的图像插值方法很好地保护了图像的细节,利用该方法进行三维重构能够有效改善重构模型的分辨力和光顺性,为提高三维表面非接触测量精度创造了条件。     

基于肤色的眼睛轮廓自动提取新方法

眼睛作为人脸识别最重要的生物特征之一,具有高稳定性、不易复制性和可区分性等特点,其特征提取是人脸图像检索的关键环节,通常对眼睛特征的描述主要依据视觉感知,面对由于模糊描述而无法实现量化处理的难题,提出了一种基于肤色的人眼轮廓自动提取新方法。该方法分为四步:首先,提取人脸彩色图像并将其从RGB色彩空间变换到YCbCr色彩空间;其次,通过肤色非线性空间映射进行人脸区域定位处理;然后,采用大津法实现人眼区域分割;最后,通过色度信息以及形态学处理方法提取眼睛轮廓。实验表明:该方法能够精确提取人眼轮廓,提取率为99%以上,提取时间小于1.75 s。     

偏振图像融合的人脸图像增强研究

为提升人脸图像的整体质量，便于人脸图像的快速精准识别与处理，提出了基于偏振图像融合的人脸图像增强方法。首先获得人脸光强图像与偏振度图像，结合小波包变换方法分解和融合两种人脸图像的高、低频信息，实现人脸图像的增强处理。实验结果表明，该方法所获得的人脸增强图像可保留原有图像的低频信息，增强后的人脸图像清晰度高，脸部细节与轮廓特征明显，信息量与反差对比度较高，整体综合质量较好，增强效果较为理想。     

FPGA实现的视频图像缩放显示

对几种常用的图像缩放算法进行了比较,在权衡了算法复杂度、缩放效果和FPGA逻辑资源等3大因素后,选择了双线性插值算法来实现图像缩放。重点介绍了双线性插值算法和该方法的FPGA硬件实现方法,包括图像数据缓冲单元、插值系数生成单元以及插值计算单元等。应用结果表明,双线性插值算法及其硬件实现模块达到了预期的效果。     

基于LED与肌肉模型的特定人脸建模与动画

特定三维人脸的建模与动画是计算机图形学中一个非常令人感兴趣的领域.本文提出了一种新的从两幅正交照片建立特定人脸的模型以及动画方法,首先以主动轮廓跟踪技术snake自动获取人脸特征点的准确位置,然后以文中的局部弹性变形(local elastic deformation)方法进行通用人脸模型到特定人脸的定制,并辅以采用图像镶嵌技术生成的大分辨率纹理图像施行纹理绘制,该方法以特征点的位移和非特征点与特征点的相对位置为基础计算局部人脸面部的变形,同时还能够实现人脸剧烈的面部变化和动作,与肌肉模型相结合,可很好地实时完成人脸的动画,具有快速高效的特点.最后,给出了所得到的实验结果.     

基于多帧二维动画图像的三维自动生成技术

针对传统的三维自动生成方法存在效果差的问题,提出基于对象层次与表面层次随动渲染技术的多帧二维动画图像三维自动生成技术。采用相似度度量方法进行图形渲染的表面层次平滑处理,利用对象层次与表面层次随动信息融合方法进行动画图像渲染,实现多帧二维动画图像的三维重构与动态生成。仿真结果表明,采用改进方法进行多帧二维动画图像重构生成,图像具有较高的逼真性。     

Automatic face cloning and animation using real-time facial featuretracking and speech acquisition

We describe the components of the system used for real-time facial communication using a cloned head. We begin with describing the automatic face cloning using two orthogonal photographs of a person. The steps in this process are the face model matching and texture generation. After an introduction to the MPEG-4 parameters that we are using, we proceed with the explanation of the facial feature tracking using a video camera. The technique requires an initialization step and is further divided into mouth and eye tracking. These steps are explained in detail. We then explain the speech processing techniques used for real-time phoneme extraction and subsequent speech animation module. We conclude with the results and comments on the integration of the modules towards a complete system     

一种人脸姿势估计新方法

本文提出了一种人脸姿势估计方法,基于单幅人脸图像的五个特征点:左右外眼角,左右嘴角和鼻尖估计人脸姿势,论文首先给出人脸姿势表示——旋转角度((?),ψ,θ),然后介绍了姿势估计方法,最后的实验结果证明了本文算法的有效性。     

Lifelike talking faces for interactive services

Lifelike talking faces for interactive services are an exciting new modality for man-machine interactions. Recent developments in speech synthesis and computer animation enable the real-time synthesis of faces that look and behave like real people, opening opportunities to make interactions with computers more like face-to-face conversations. This paper focuses on the technologies for creating lifelike talking heads, illustrating the two main approaches: model-based animations and sample-based animations. The traditional model-based approach uses three-dimensional wire-frame models, which can be animated from high-level parameters such as muscle actions, lip postures, and facial expressions. The sample-based approach, on the other hand, concatenates segments of recorded videos, instead of trying to model the dynamics of the animations in detail. Recent advances in image analysis enable the creation of large databases of mouth and eye images, suited for sample-based animations. The sample-based approach tends to generate more naturally looking animations at the expense of a larger size and less flexibility than the model-based animations. Beside lip articulation, a talking head must show appropriate head movements, in order to appear natural. We illustrate how such "visual prosody" is analyzed and added to the animations. Finally, we present four applications where the use of face animation in interactive services results in engaging user interfaces and an increased level of trust between user and machine. Using an RTP-based protocol, face animation can be driven with only 800 bits/s in addition to the rate for transmitting audio.     

Facial parts swapping with generative adversarial networks

In this paper, we present a novel deep generative facial parts swapping method: parts-swapping generative adversarial network (PSGAN). PSGAN independently handles facial parts, such as eyes (left eye and right eye), nose, mouth and jaw, which achieves facial parts swapping by replacing the target facial parts with source facial parts and reconstructing the entire face image with these parts. By separately modeling the facial parts in the form of region inpainting, the proposed method can successfully achieve highly photorealistic face swapping results, enabling users to freely manipulate facial parts. In addition, the proposed method is able to perform jaw editing based on sketch guidance information. Experimental results on the CelebA dataset suggest that our method achieves superior performance for facial parts swapping and provides higher user control flexibility.     

Conversion of articulatory parameters into active shape model coefficients for lip motion representation and synthesis

Speech-driven facial animation combines techniques from different disciplines such as image analysis, computer graphics, and speech analysis. Active shape models (ASM) used in image analysis are excellent tools for characterizing lip contour shapes and approximating their motion in image sequences. By controlling the coefficients for an ASM, such a model can also be used for animation. We design a mapping of the articulatory parameters used in phonetics into ASM coefficients that control nonrigid lip motion. The mapping is designed to minimize the approximation error when articulatory parameters measured on training lip contours are taken as input to synthesize the training lip movements. Since articulatory parameters can also be estimated from speech, the proposed technique can form an important component of a speech-driven facial animation system.     

3D facial model reconstruction, expressions synthesis and animation using single frontal face image

With better understanding of face anatomy and technical advances in computer graphics, 3D face synthesis has become one of the most active research fields for many human-machine applications, ranging from immersive telecommunication to the video games industry. In this paper we proposed a method that automatically extracts features like eyes, mouth, eyebrows and nose from the given frontal face image. Then a generic 3D face model is superimposed onto the face in accordance with the extracted facial features in order to fit the input face image by transforming the vertex topology of the generic face model. The 3D-specific face can finally be synthesized by texturing the individualized face model. Once the model is ready six basic facial expressions are generated with the help of MPEG-4 facial animation parameters. To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models and blend the corresponding textures. Novelty of our method is automatic generation of 3D model and synthesis face with different expressions from frontal neutral face image. Our method has the advantage that it is fully automatic, robust, fast and can generate various views of face by rotation of 3D model. It can be used in a variety of applications for which the accuracy of depth is not critical such as games, avatars, face recognition. We have tested and evaluated our system using standard database namely, BU-3DFE.     

基于Gabor小波和SVM的人脸表情识别算法

针对包含表情信息的静态图像,提出基于Gabor小波和SVM的人脸表情识别算法。根据先验知识,并使用形态学和积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域。对分割出的表情子区域进行Gabor小波特征提取,在利用Fisher线性判别对特征进行降维,去除冗余和相关。利用支持向量机对人脸表情进行分类。用该算法在日本表情数据库上进行测试,获得了较高的识别准确率。证明了该算法的有效性。     

基于OPENCV的人脸考勤系统

设计实现了一种基于几何特征的人脸识别考勤系统。通过图像的灰度积分投影来确定各器官的大体位置。通过提取人脸的左眼、右眼、鼻子和嘴巴的位置及其之间的相对距离关系等7个特征值,并赋予不同的权重来合成矢量人脸,进而与数据库进行对比实现人脸考勤。     

A new efficient expression generation and automatic cloning method for multimedia actors

This paper describes a new and efficient method for facial expression generation on cloned synthetic head models. The system uses abstract facial muscles called action units (AUs) based on both anatomical muscles and the facial action coding system. The facial expression generation method has real-time performance, is less computationally expensive than physically based models, and has greater anatomical correspondence than rational free-form deformation or spline-based, techniques. Automatic cloning of a real human head is done by adapting a generic facial and head mesh to Cyberware laser scanned data. The conformation of the generic head to the individual data and the fitting of texture onto it are based on a fully automatic feature extraction procedure. Individual facial animation parameters are also automatically estimated during the conformation process. The entire animation system is hierarchical; emotions and visemes (the visual mouth shapes that occur during speech) are defined in terms of the AUs, and higher-level gestures are defined in terms of AUs, emotions, and visemes as well as the temporal relationships between them. The main emphasis of the paper is on the abstract muscle model, along with limited discussion on the automatic cloning process and higher-level animation control aspects.     

基于ARM的防疲劳驾驶检测系统

基于ARM的防疲劳驾驶检测系统,采用USB摄像头采集驾驶员的面部图像并将图像转换为数字信号传给ARM处理器。采集的面部图像通过模板匹配算法用特定的眼部分类器进行计算,与眼部分类器配比,在指定的矩形区域中提取出眼部图像,计算眼部图像的灰度直方图。通过对驾驶员的眼部闭合状态的实时分析可以获得PERCLOS参数,从而判断驾驶员的驾驶疲劳状态。经实验测试证明该方法准确性高,能在多种情况下检测驾驶员是否处于驾驶疲劳状态。