2D facial expression recognition via 3D reconstruction and feature fusion

In this paper, a novel feature extraction method is proposed for facial expression recognition by extracting the feature from facial depth and 3D mesh alongside texture. Accordingly, the 3D Facial Expression Generic Elastic Model (3D FE-GEM) method is used to reconstruct an expression-invariant 3D model from the human face. Then, the texture, depth and mesh are extracted from the reconstructed face model. Afterwards, the Local Binary Pattern (LBP), proposed 3D High-Low Local Binary Pattern (3DH-LLBP) and Local Normal Binary Patterns (LNBPs) are applied to texture, depth and mesh of the face, respectively, to extract the feature from 2D images. Finally, the final feature vectors are generated through feature fusion and are classified by the Support Vector Machine (SVM). Convincing results are acquired for facial expression recognition on the CK+, CK, JAFFE and Bosphorus image databases compared to several state-of-the-art methods.     

Gabor字典及l0范数快速稀疏表示的人脸识别算法

针对光照、遮挡、伪装情况下,识别率比较低,识别时间长的问题,本文提出了基于Gabor字典及l0范数快速稀疏表示的人脸识别算法。Gabor小波提取的特征能够克服遮挡、光照等干扰对人脸识别的影响,平滑l0算法通过平滑连续函数来近似 l0范数,只需较少测量值并且较快速度便能重构稀疏信号。本算法通过提取人脸的Gabor特征、主成分分析法（PCA）降低维度,l0范数快速稀疏分类完成识别。在伪装人脸情况下,分块计算Gabor人脸特征,提高Gabor字典的形成速度。基于AR人脸数据库的实验结果表明,本算法可在一定程度上提高识别速度和识别时间,即使在小样本情况下,依然具有较高的识别率。      

TDFF:一种强鲁棒性的烟雾图像检测算法

烟雾图像检测是及早发现火灾的一种重要手段。针对传统LBP(Local Binary Patterns)特征与Gabor特征的融合算法存在鲁棒性和检测率低的问题,提出一种TDFF(Triple Multi Feature Local Binary Patterns and Derivative Gabor Feature Fusion)的烟雾检测算法。采用T-MFLBP(Triple Multi Feature Local Binary Patterns)算法分别对像素间不同灰度差值以及非均匀模式中特殊位置的像素进行编码计算,可以捕捉更清晰的纹理特征;然后利用高斯核函数的一阶偏导数提取Gabor特征,从而优化提取图像边缘灰度信息的性能;最后对融合后的特征进行训练,可以提高最终分类的准确性。实验结果表明,TDFF算法具有较强的鲁棒性,烟雾图像的检测率也显著优于未改进的传统算法。     

基于Gabor滤波的表情动态特征提取方法

针对目前动态特征提取方法在提取序列表情特征时人脸外貌特征也一起被提取的缺陷,提出了一种基于Gabor滤波的表情动态特征提取方法。利用Gabor滤波器在频率和方向上的选择特性,在提取表情特征时较好地抑制了人脸外貌特征的提取,从而减少了表情特征中人脸外貌特征的含量。在Cohn-Kanade和CMU-AMP人脸库上的表情识别实验表明,本文方法获得的表情动态特征对表情识别更有效。     

Face Recognition Using Dual-Tree Complex Wavelet Features

We propose a novel facial representation based on the dual-tree complex wavelet transform for face recognition. It is effective and efficient to represent the geometrical structures in facial image with low redundancy. Moreover, we experimentally verify that the proposed method is more powerful to extract facial features robust against the variations of shift and illumination than the discrete wavelet transform and Gabor wavelet transform.     

基于Gabor和PCA的面部图像识别方法

文中利用Gabor变换和PCA降维的优点,提出了Gabor+PCA的面部图像识别方法.该方法先提取图像的Gabor特征,然后将Gabor特征与原图像特征结合构成新的融合特征并用PCA降维,最后用KNN分类器分类.所提Gabor+PCA方法不仅能挖掘出图像的细节信息,而且拓宽了特征空间的维数.另外,Gabor+PCA方法...     

基于直方图统计量的逆合成孔径雷达目标识别

将原用于人脸识别的基于Gabor局部二进制模式的识别技术用于逆合成孔径雷达(ISAR)像的雷达目标识别,对算法进行了改进,取得了较好的识别效果。将ISAR像进行Gabor小波变换,提取不同尺度和方向的Gabor幅值图谱;然后把幅值图谱分成小的子区域,用多尺度局部二值模式提取空域增强的直方图作为特征,最后在χ2统计量作为不相似度量计算的特征空间里,采用最近邻分类器完成五类目标的分类识别。与目前已有的几种典型ISAR目标识别方法进行了对比,结果表明:该方法是可行且有效的,能够明显地提高识别率。     

Efficient facial landmark localization using spatial–contextual AdaBoost algorithm

Facial landmark detectors can be categorized into global and local detectors. Global facial landmark detectors rely on global statistical relations between landmarks, but do not sufficiently utilize local appearance information, whereas local detectors mainly focus on local appearance attributes of landmarks. Although the AdaBoost algorithm has been successfully employed in object localization, it cannot take advantage of geometric facial feature distribution very well. We propose an AdaBoost algorithm called SC-AdaBoost, which efficiently combines the global knowledge of landmark distribution, the regional shape model, and the local landmark attributes based on a coarse-to-fine strategy. The global prior distribution of landmarks is estimated using a face image set with landmark annotations. First, the face region is detected as a rectangular bounding box using a Haar-like feature-based boosting method, and the global distribution of landmarks is used to determine the facial component regions. Facial landmark localization is roughly performed by regional shape modeling. Posteriors of individual weak classifiers are determined by Gabor wavelet analysis at landmark candidate positions constrained by the regional shape model. SC-AdaBoost is established by empirical risk minimization, which decides the weights for the weak classifiers, and is used for the precise localization. The strength of the proposed approach is shown by extensive experiments using standard face datasets.     

Robust face anti-spoofing with depth information

With the prevalence of face authentication applications, the prevention of malicious attack from fake faces such as photos or videos, i.e., face anti-spoofing, has attracted much attention recently. However, while an increasing number of works on the face anti-spoofing have been reported based on 2D RGB cameras, most of them cannot handle various attacking methods. In this paper we propose a robust representation jointly modeling 2D textual information and depth information for face anti-spoofing. The textual feature is learned from 2D facial image regions using a convolutional neural network (CNN), and the depth representation is extracted from images captured by a Kinect. A face in front of the camera is classified as live if it is categorized as live using both cues. We collected a face anti-spoofing experimental dataset with depth information, and reported extensive experimental results to validate the robustness of the proposed method.     

自适应局部综合特征加权的单样本人脸特征提取

为了有效地提取单训练人脸样本的特征,提出了一种新的人脸局部特征描述方法,改进了局部二进制模式的方向性描述单一的问题,并且加入了像素间变化趋势幅度的描述,称之为局部综合模式(Local Comprehensive Patterns,LCP).首先对人脸样本图像进行分块,然后每个的分块子图像进行改进LCP算子运算;其次考虑到每个子块的特征对整幅人脸图像的贡献度不一致,提出了贡献度图谱(Contribution Map,CM);最后根据贡献度图谱对每个子块的改进LCP描述进行自适应加权融合形成最终的人脸描述特征,最后在ORL和Yale B库上进行了有效性的测试,与现有的多种算法进行比对,所提出的算法对于非限定环境下人脸的识别有良好的效果.     

Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained.     

基于Gabor小波变换和PCA的人脸识别方法

基于Gabor小波在图像表征方面的优越性,阐述了将Gabor小波和主分量分析(PCA)相结合用于人脸识别的方案。对人脸图像进行Gabor小波变换,通过PCA(主分量分析)降维后,计算特征点之间的距离,最后进行人脸识别。     

Facial micro-expression recognition based on accordion spatio-temporal representation and random forests

Micro-expressions are very brief involuntary facial expressions which appear on the face of humans when they unconsciously conceal an emotion. Creating a solution allowing an automatic recognition of the facial micro-expressions from video sequences has garnered increasing attention from experts across such different disciplines as computer science, security, and psychology. This paper offered a solution to facial micro-expressions recognition, based on accordion spatio-temporal representation and Random Forests. The proposed feature space, called “Uniform Local Binary Patterns on an Accordion 2D representation of sub-regions presented by a Pyramid of levels (LBPAccP^u2)”, exploits the effectiveness of uniform LBP patterns applied on an accordion representation of sub-regions at different sizes. Random Forests were used to select the most discriminating features and reduce the classification ambiguity of similar micro-expressions through a new proximity measure. The main objective of our paper was to demonstrate that the use of few features could be more efficient to produce a strong micro-expression recognition classifier that outperforms the approaches that rely on high dimensional features space. The experimental results across six micro-expression datasets show the effectiveness of the proposed solution with an accuracy rate that can reach 81.38% on CasmeII dataset. Compared to some famous competitive state-of-the-art approaches, the proposed solution proved its performance thanks to its accuracy rate as well as the number of features it uses.     

多方向多尺度Gabor特征表示及其匹配算法

Gabor滤波是众所周知的一类特征提取方法,在机器视觉等领域得到了广泛研究和应用.本文提出了一种多方向多尺度Gabor特征表示、提取以及其匹配算法.多方向多尺度Gabor特征通过使用一组不同尺度和不同方向的Gabor滤波器对图像进行滤波,而后将滤波结果在各个滤波方向按尺度大小排序后连接而成.本文进一步提出了循环向量的概念,并将两个多方向多尺度Gabor特征相似度重新定义为一个多方向多尺度Gabor特征和对应的多个循环向量之间最大值.实验结果表明,本文提出的多方向多尺度Gabor特征不仅具有平移不变性、旋转不变性、尺度不变性,也展现出优秀的局部特征表示能力以及显著的鉴别力.     

Quality assessment of screen content images based on multi-stage dictionary learning

In this paper, we propose an effective method for quality assessment of screen content images (SCIs) based on multi-stage dictionary learning. To simulate the brain’s layered processing of signals, we proposed a hierarchical feature extraction strategy, which is called multi-stage dictionary learning, to simulate the hierarchical information processing of brain. First, the standard deviation of normalized map obtained from training image is used to select the training data in a certain proportion, which can ensure the learning efficiency and reduce the training burden. Next, the reconstructed map is weighted as the input of the next-stage dictionary learning. Then using the trained dictionary, the sparse representation is applied to extract features. Meanwhile, considering that some important features may be ignored in the process of multi-stage dictionary learning, we use Log Gabor filter to extract feature maps, and then calculate the correlation between feature maps as another kind of compensation features. Final, for the two feature sets, we choose SVR and feature codebook to learn two objective scores, and then use the adaptive weighting strategy to get the final objective quality score. Experimental results show that the proposed method is superior to several mainstream SCIs metrics on two publicly available databases.     

基于Gabor小波变换的人脸表情识别

在人脸表情识别中,针对Gabor小波变换特征维数很大的问题,提出了一种新的多方向特征编码方法。通过对Gabor特征幅值进行统计处理,将每个像素点同一尺度不同方向的Gabor特征幅值闽值化成二进制,加强了Gabor小波对图像局部结构信息的表征。同时,结合了类似旋转不变LBP的方法对图像进行降维。为了进一步提高表情的正确识别率,采用一种局部区域融合的方法,最后在JAFFE表情库上进行测试,得到比较好的识别率,验证了所提方法的有效性。     

融合局部纹理和形状特征的人脸表情识别

针对局部二值模式(LBP)、中心对称局部二值模式(CS-LBP)和梯度方向直方图(HOG)的不足进行改进,该文提出中心对称局部平滑二值模式(CS-LSBP)和绝对梯度方向直方图(HOAG),并提出一种融合局部纹理特征和局部形状特征的人脸表情识别方法。该方法首先采用CS-LSBP算子和HOAG算子分别提取人脸表情图像的局部纹理特征和局部形状特征,然后使用典型线性分析法(CCA)进行特征融合,最后利用支持向量机(SVM)进行表情分类。在JAFFE人脸表情库和Cohn-Kanade(CK)人脸表情库上的实验结果表明,改进的特征提取方法能更加完整、精确地提取图像的细节信息,基于CCA的特征融合方法能充分发挥特征的表征能力,该文所提人脸表情识别方法取得了较好的分类识别效果。     

基于图像去模糊的改进Gabor与LSSVM的人脸识别

为有效解决人脸识别中二维Gabor的维数灾难,LDA的小样本问题和因拍摄不慎造成的图像模糊的问题,提出一种图像去模糊的改进Gabor和最小二乘支持向量机(LSSVM)相结合的新算法。首先用约束最小二乘方(CLS)对模糊的人脸图像去模糊,然后将DLDA和二维Gabor相融合进行降维处理,最后利用训练速度快,泛化能力强的LSSVM进行分类识别。并通过ORL和Yale人脸库来做对比验证,证明了此方法的高效性。