基于改进光流算法和HMM的面部表情识别

提出了一种基于改进光流和HMM的面部表情识别算法,在Lucas-Kanade光流法中引入Hessian矩阵,可有效消除局部邻域中不可靠约束点,提高面部表情变化的光流场计算精度.同时采用GA算法对HMM参数重估,有效提高了HMM的分类性能.实验结果表明,该算法取得了较好的识别效果.     

一种改进的HMM训练算法及其在面部表情识别中的应用

本文讨论了最大模型距离准则,对原有最大模型距离准则函数做了合理的修正,并把它应用于隐马尔可夫模型的训练,重新推导了HMM的迭代公式,使得训练后的HMM具有更好的鉴别能力。把这种改进的HMM训练算法应用于面部表情识别,利用本文给出的改进光流算法提取面部表情特征向量序列,构建了基于改进的HMM算法和BP神经网络的面部表情混合分类器,通过实验表明识别率得到有效提高。     

基于遗传算法的直线光流刚体运动重建

建立一种新的基于直线光流场从单目图像序列恢复刚体运动和结构的模型,推导出直线光流场与刚体的运动参数之间的关系,用2个二阶线性微分方程表达这种关系,并提出一种求解刚体运动参数的遗传算法,只需要获得图像平面的2条直线光流即可求解刚体的旋转参数,并用合成图像测试了该算法的有效性。     

基于支持向量机的实时表情识别

从实时图像中识别面部表情和推断情感是一个极具挑战性的研究课题。文章介绍了一种根据视频图像对面部表情进行实时识别的方法。使用ASM方法和改进的L-K光流算法进行面部特征定位和特征跟踪,提取的面部特征位移作为支持向量机分类器的输入。实验证明,支持向量机和特征跟踪的方法能很好地识别面部表情。     

基于特征流的面部表情运动分析及应用

面部表情的分析与识别,不但在社会生活中具有普遍意义,而且在计算机的情感计算方面也起着有重要作用.关于表情运动特征的分析,有根据人脸面部几何结构特征的变化来分析的,有根据特征脸的概念定义的"表情空间"来分析的,也有从特征点跟踪的方法或运动模板的角度来分析的.基于人脸面部物理-几何结构模型,提取面部表情特征区域,通过动态图像序列中的光流估计,计算其运动场,进而计算特征流向量,把一组图像序列的运动向量组成运动特征序列,对表情的运动进行分析.该系统作为一个智能体应用到多功能感知机中,作为视频通道输入的一部分来理解人类的体势语言信息.     

一种结合光流法与三帧差分法的运动目标检测算法

运动目标的检测是计算机视觉研究的重要内容之一,光流法是其中的一种重要方法.由于计算光流的算法复杂,限制了它的使用.本文提出一种结合光流法与三帧差分法的运动目标检测算法,该算法简化了光流的计算,选择图像中具有代表性的Harris角点,只对这些像素点计算光流信息,有效地减少了复杂度,由于检测得到的运动目标区域不够完整,引入了三帧差分法作为简化光流法的补充.经过实验,该方法使光流法达到了实时性要求,取得了好的效果,优于单独运用两种方法中的任何一种取得的效果.     

基于纹理约束和参数化运动模型的光流估计

提出了一种基于局部小平面运动的光流估计新方法。目的是获得精确致密的光流估计结果。与以往采用亮度一致性区域作为假设平面的算法不同，本算法利用序列图像的纹理信息，在纹理分割区域的基础上，进行运动估计。该算法首先通过微分法计算粗光流，可以得到参数化光流模型的初始估计，然后通过区域迭代算法，调整初始估计，从而得到精细的平面分割及其对应的参数化光流模型。基于纹理信息的部分拟合算法被用于算法的每一步当中，保证了纹理边缘位置的光流估计值的准确性。实验采用了标准图像序列，结果表明，可以得到更为精细的光流估计结果，特别是对于那些有着丰富纹理信息的室外环境的图像序列，而且在运动边界处的结果改善尤为明显。     

基于图像的动目标检测技术

本文研究了光流估计的基本算法，从运动分割的角度的提出了使用光流分割方案来解决复杂背景下的动目标检测问题。文中分析了标准光流估计的缺陷及其原因，并使用鲁棒性估计技术提高了光流估计算法的实用性。最后给出了动目标检测算法的仿真结果，表明利用图像的运动特征来实现目标检测是一种很有效的方法。     

基于特征三角形的多姿态视频图像人脸跟踪

提出了一种能在复杂环境中进行人脸跟踪的鲁棒、有效的视频图像人脸跟踪的算法。该算法根据面部特征构造特征三角形包括等腰三角形和直角三角形，根据刚体约束生成潜在人脸跟踪矩形区域。该算法能够在不同尺寸、不同光照、不同姿态和不同表情甚至不同噪音情况下检测人脸，有效率达98.18%。     

一种基于背景运动特性分析的背景配准算法

为了解决复杂背景下基于背景运动补偿的目标检测算法无法精确提取背景运动特征块的问题,该文提出了一种基于背景运动特性分析的背景配准算法。采用Harrir-Affine焦点算法提取仿射不变特征点,通过Lucas-Kande光流法估算特征区域光流矢量,通过特征区域光流主方向相似度度量对特征区域聚类实现背景特征区域的精确提取。实验结果表明:该算法能够精确提取出背景运动特征块,与传统的基于背景配准的目标检测算法进行比较,采用本文算法的目标检测其有效性提高了1.1倍。     

3-D motion estimation in model-based facial image coding

An approach to estimating the motion of the head and facial expressions in model-based facial image coding is presented. An affine nonrigid motion model is set up. The specific knowledge about facial shape and facial expression is formulated in this model in the form of parameters. A direct method of estimating the two-view motion parameters that is based on the affine method is discussed. Based on the reasonable assumption that the 3-D motion of the face is almost smooth in the time domain, several approaches to predicting the motion of the next frame are proposed. Using a 3-D model, the approach is characterized by a feedback loop connecting computer vision and computer graphics. Embedding the synthesis techniques into the analysis phase greatly improves the performance of motion estimation. Simulations with long image sequences of real-world scenes indicate that the method not only greatly reduces computational complexity but also substantially improves estimation accuracy     

Superimposed training for channel estimation of OFDM modulated amplify-and-forward relay networks

This paper is concerned with the problem of channel estimation for amplify-and-forward （AF） cooperative relay networks with orthogonal frequency division multiplexing （OFDM） modulation. The algorithm is based on both the least square （LS） and minimum mean square error （MMSE） technique with a superimposed training strategy. Specifically, both the source and relay superimpose their own training signal onto data stream prior to transmission so as to estimate the separate channel state information of the source to relay link and the relay to destination link. We also present the performance analysis and derive the approximated closed-form expressions for the MSE of separate channel estimation of source to relay link and the relay to destination link, respectively, from which we compute the optimal training signal as well as the relay power-amplification factor. To further improve the performance of channel estimation, we adopt a weighted average process to enhance the estimation performance over multiple OFDM blocks, from which we compute the optimal tracking factor. Simulation results are provided to corroborate the proposed scheme.     

基于特征运动的表情人脸识别

人脸像的面部表情识别一直是人脸识别的一个难点，为了提高表情人脸识别的鲁棒性，提出了一种基于特征运动的人脸识别方法，该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量，然后利用主成分分析方法（PCA）从这些运动向量中，产生低维子空间，称之为特征运动空间，测试时，先将测试人脸与无表情人脸之间的运动向量投影到特征运动空间，再根据这个运动向量在特征运动空间里的残差进行人脸识别，同时还介绍了基于特征运动的个人模型方法和公共模型方法，实验结果证明，该新算法在表情人脸的识别上，优于特征脸方法，有非常高的识别率。     

Coding, analysis, interpretation, and recognition of facialexpressions

We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with geometric, physical and motion-based dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the facial action coding system (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate, representation of human facial expressions that we call FACS+. Finally, we show how this method can be used for coding, analysis, interpretation, and recognition of facial expressions     

Facial expression recognition and tracking for intelligent human-robot interaction

For effective interaction between humans and socially adept, intelligent service robots, a key capability required by this class of sociable robots is the successful interpretation of visual data. In addition to crucial techniques like human face detection and recognition, an important next step for enabling intelligence and empathy within social robots is that of emotion recognition. In this paper, an automated and interactive computer vision system is investigated for human facial expression recognition and tracking based on the facial structure features and movement information. Twenty facial features are adopted since they are more informative and prominent for reducing the ambiguity during classification. An unsupervised learning algorithm, distributed locally linear embedding (DLLE), is introduced to recover the inherent properties of scattered data lying on a manifold embedded in high-dimensional input facial images. The selected person-dependent facial expression images in a video are classified using the DLLE. In addition, facial expression motion energy is introduced to describe the facial muscle’s tension during the expressions for person-independent tracking for person-independent recognition. This method takes advantage of the optical flow which tracks the feature points’ movement information. Finally, experimental results show that our approach is able to separate different expressions successfully.     

Qualitative estimation of camera motion parameters from the linear composition of optical flow

In this paper, we propose a new method for estimating camera motion parameters based on optical flow models. Camera motion parameters are generated using linear combinations of optical flow models. The proposed method first creates these optical flow models, and then linear decompositions are performed on the input optical flows calculated from adjacent images in the video sequence, which are used to estimate the coefficients of each optical flow model. These coefficients are then applied to the parameters used to create each optical flow model, and the camera motion parameters implied in the adjacent images can be estimated through a linear composition of the weighted parameters.We demonstrated that the proposed method estimates the camera motion parameters accurately and at a low computational cost as well as robust to noise residing in the video sequence being analyzed.     

基于水平集的多运动目标时空分割与跟踪

针对背景运动时的运动目标分割问题,提出了一种对视频序列中的多个运动目标进行分割和跟踪的新方法。该方法着眼于运动的且较为复杂的背景,首先利用光流约束方程和背景运动模型建立一个基于时空域的能量函数,然后用该函数进行背景运动速度的估算和运动目标的分割和跟踪。而时空域中的运动目标的最佳分割,乃是通过使该能量函数最小化来驱动时空曲面演化实现。时空曲面的演化采用了水平集PDEs(Partial Differential Equations)方法。实验中,用实际的图像序列验证了该算法及其数值实现。实验表明,该方法能够同时进行背景运动速度的估算、运动目标的分割和跟踪。     

Shape and motion from simultaneous equations with closed-loop solution

This paper proposes a method for simultaneously estimating 2D image motion and 3D object shape and motion from only two frames. The problem is formulated in a system of equations, including the differential epipolar constraint, a newly derived optical flow equation and surface normal constraint, under the assumption of perspective projection, rigid motion, Lambertian reflectance and distant lighting. A closed-loop solver is constructed based on the simultaneous equations to export accurate estimate for optical flow as well as dense shape and motion. Experimental results are also provided.     

Face authentication for multiple subjects using eigenflow

In this paper, we present a novel scheme for face authentication. To deal with variations, such as facial expressions and registration errors, with which traditional intensity-based methods do not perform well, we propose the eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and an image in the training set are first computed. The optical flow is then fitted to a model that is pre-trained by applying principal component analysis to optical flows resulting from facial expressions and registration errors for the subject. The eigenflow residue, optimally combined with the optical flow residue using linear discriminant analysis, determines the authenticity of the test image. An individual modeling method and a common modeling method are described. We also present a method to optimally choose the threshold for each subject for a multiple-subject authentication system. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of facial expression variations and registration errors.