Face recognition based on PCA image reconstruction and LDA

Face recognition has become a research hotspot in the field of pattern recognition and artificial intelligence. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two traditional methods in pattern recognition. In this paper, we propose a novel method based on PCA image reconstruction and LDA for face recognition. First, the inner-classes covariance matrix for feature extraction is used as generating matrix and then eigenvectors from each person is obtained, then we obtain the reconstructed images. Moreover, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The simulation experiments illustrate the effectivity of this method on the ORL face database.     

基于自适应正则化的超分辨率重建方法

传统正则化超分辨重建得到的图像往往存在过度平滑或伪信息残留的问题,结合超分辨重建模型对重建图像伪信息的产生进行了分析,针对传统方法的不足提出了基于图像区域信息自适应的正则化方法,通过图像的区域信息将图像划分为平滑区与非平滑区域,对不同区域选用不同的先验模型进行约束。同时考虑人眼的视觉感知特性,结合区域信息实现正则化参数的自适应选取。实验结果表明该方法在抑制重建图像伪信息的同时能有效保护细节,效果要优于传统方法与单一的先验模型约束,对于红外与可见光图像重建效果的提升提供了一定的理论参考。     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.     

A face recognition algorithm based on collaborative representation

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error (RRE) of each class is the division of the l₂-norm of reconstruction error to the l₂-norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases.     

A sparse representation method based on kernel and virtual samples for face recognition

To improve the classification accuracy of face recognition, a sparse representation method based on kernel and virtual samples is proposed in this paper. The proposed method has the following basic idea: first, it extends the training samples by copying the left side of the original training samples to the right side to form virtual training samples. Then the virtual training samples and the original training samples make up a new training set and we use a kernel-induced distance to determine M nearest neighbors of the test sample from the new training set. Second, it expresses the test sample as a linear combination of the selected M nearest training samples and finally exploits the determined linear combination to perform classification of the test sample. A large number of face recognition experiments on different face databases illustrate that the error ratios obtained by our method are always lower more or less than face recognition methods including the method mentioned in Xu and Zhu [21], the method proposed in Xu and Zhu [39], sparse representation method based on virtual samples (SRMVS), collaborative representation based classification with regularized least square (CRC_RLS), two-phase test sample sparse representation (TPTSSR), and the feature space-based representation method.     

Robust face recognition based on sparse representation in 2D Fisherface space

Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness.     

稀疏阈值的超分辨率图像重建

为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1 d B,平均结构自相似度(SSIM)为0.937 9,平均计算时间为10.2 s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。     

Augmented Lanczos bidiagonalization by small singular value decompositions for face recognition and image compression

A new method for face recognition and image compression is presented here. In this method, Lanczos bidiagonalization is combined with sequences of small singular value decompositions. The goal of this method is to perform the truncated singular value decomposition for large matrices. It is an extension of standard Lanczos bidiagonalization, which improves its approximation capabilities and keeps the computational cost reasonable. Face recognition and image compression examples show the new method is competitive with pervious methods.     

Face image recognition via collaborative representation on selected training samples

In this paper, we present a collaborative representation-based classification on selected training samples (CRC_STS) for face image recognition. The CRC_STS uses a two stage scheme: The first stage is to select some most significant training samples from the original training set by using a multiple round of refining process. The second stage is to use collaborative representation classifier to perform classification on the selected training samples. Our method can be regarded as a sparse representation approach but without imposing l₁-norm constraint on representation coefficients. The experimental results on three well known face databases show that our method works very well.     

基于梯度阈值自适应处理的红外图像超分辨率重建

超分辨率图像重建中,Huber马尔可夫随机场模型是一种常用的正则化算子.针对Huber函数中固定梯度阈值引起图像重建效果不佳的问题,本文提出一种梯度阈值自适应处理的红外图像超分辨率重建算法.在最大后验概率理论框架下,构造了基于数据项和正则项的正则化模型;通过迭代的方式,利用中间重建结果不断更新正则化参量,解决了Huber马尔可夫随机场模型中梯度阈值不易选择的难题.实验结果表明,改进算法能够根据局部梯度特征自适应选择相应的正则化参量并找到最优解,较好恢复目标细节的同时有效抑制了图像噪音.     

Study of mutual information multimodality medical image registration based on modified simplex optimization method

Because of a different imaging mechanism and highly complexity of body tissues and structures. Different modality medical images provide non-overlay complementary information. This has very important significance for multimodal medical image registration. Image registration is the first and key part of problem to be solved in the integrations. When the spatial position of two medical images is same, the registration could be achieved. For two CT and PET images, the principal axis method is adopted to achieve the rough registration. The modified simplex algorithm is employed to implement global search using the mutual information as similarity measure. The initial registration parameters are achieved through principal axis Based on the results of test, improved simplex method can adjust reflecting distance. Stepped-up optimization algorithm on the new experimental points through the methods of “reflection”, “enlargement”, “shrinkage” or “global systolic”. A mutual information registration based on modified simplex optimization method is presented in this paper to improve the speed of medical image registration.Results indicate that the proposed registration method prevents the optimizing process from falling into local extremum and improves the convergence speed while keeping the precision. The accurate registration of multimodal image with different resolutions is achieved.     

基于不可控微扫描的高分辨力图像重构方法

针对存在不可控微位移的序列低分辨力图像,提出了一种基于2×2不可控微扫描的高分辨力图像重构方法,采用投影法估算出低分辨力图像LR的微位移量,采用基于泰勒级数展开的重构算法由4帧图像重构高分辨力图像.模拟表明:该算法能精确地估计序列图像的帧间位移量并取得较好的重构效果,且算法简单有效可行,处理量小,有利于实现快速处理,在一定程度可满足同步轨道卫星、公安监视等领域对高分辨力成像处理的需求.     

Designing a composite correlation filter based on iterative optimization of training images for distortion invariant face recognition

We present a novel method to optimize the discrimination ability and noise robustness of composite filters. This method is based on the iterative preprocessing of training images which can extract boundary and detailed feature information of authentic training faces, thereby improving the peak-to-correlation energy (PCE) ratio of authentic faces and to be immune to intra-class variance and noise interference. By adding the training images directly, one can obtain a composite template with high discrimination ability and robustness for face recognition task. The proposed composite correlation filter does not involve any complicated mathematical analysis and computation which are often required in the design of correlation algorithms. Simulation tests have been conducted to check the effectiveness and feasibility of our proposal. Moreover, to assess robustness of composite filters using receiver operating characteristic (ROC) curves, we devise a new method to count the true positive and false positive rates for which the difference between PCE and threshold is involved.     

‘Symmetrical face’ based improved LPP method for face recognition

As one of the most important branches of pattern recognition and computer vision, face recognition has more and more become the focus of researches. In real word applications, the face image might have various changes owing to varying illumination, facial expression and poses, so we need sufficient training samples to convey these possible changes. However, most face recognition systems cannot capture many face images of every user for training, non-sufficient training samples have become one bottleneck of face recognition. In this paper, we propose to exploit the symmetry of the face to generate ‘symmetrical face’ samples and use an improved LPP method to perform classification. Experimental results show that our method can get a high accuracy.     

基于数字图像处理技术的人脸检测算法研究

为了提高人脸检测的准确性及检测速度,需要对基于数字图像处理技术的人脸检测算法进行研究。使用当前方法进行人脸检测时,需要提取脸部特征数目较多、检测速度过慢,降低人脸检测效率。为此,提出一种基于数字图像处理技术的人脸检测算法。该方法首先获取人脸数字图像,通过拉开数字图像的灰度间距,使数字图像灰度均匀分布,进而提高数字图像对比度,使图像更加清晰,再通过Wiener维纳滤算法对处理后的数字图像进行图像平滑去噪,在此基础上使用Robert边缘检测算子方法对数字图像人脸边缘每个像素点检测,得到数字图像中人脸边缘的基本图像,将其输入到计算机数字图像处理系统中进行识别检测。实验仿真证明,所提算法在检测速度及准确性等方面具有明显的优势。     

基于L1范数和正交梯度算子的超分辨率重建

针对超分辨率图像重建的病态问题,设计了一种新的自适应超分辨率图像序列重建算法。该算法在L1范数重建框架下,利用金字塔算法与Lucas-Kanade算法相结合的方法实现图像配准,获得亚像素的运动估计;通过引入移位算子给出了基于正交梯度算子的正则项的实现方法,并从自适应的角度选择正则化参数,最后通过最速下降法求解模型的目标泛函最小值。结果表明:对于模拟实验和真实序列实验,该方法相比于样条插值算法、Tikhonov正则化算法、双边全变差重建算法都有一定的优势,能够取得更好的复原效果,并且由于正则项较为简单,重建所需时间相对减少。     

基于算术-几何均值距离的多模态图像配准

根据图像灰度联合概率分布函数与图像相似程度之间的关系,提出了一种基于算术 几何均值距离的多模态图像配准新测度。与基于信息论的测度不同,新测度不再要求概率分布必须满足连续性的要求。实验结果表明,所提出的新测度比基于信息论的测度具有更强的噪声鲁棒性和计算量更小。     

An improvement of image registration based on phase correlation

This paper is dedicated to studying the extended phase correlation method that applies to the sub-pixel evaluation of translational displacement between images. On the basis of experiments, this paper proposes an improved version of classical Hanning window, with involving parameters optimization in the process. We add a window function to the input images in the space domain and a weighting function to the spectrum in the frequency domain. The statistics and distribution of errors prove the applicability of the proposal. The experimental results of the proposed extended phase correlation algorithm show the registration accuracy of 1/100 pixel.     

Face recognition based on PCA and logistic regression analysis

Face recognition is an important research hotspot. More and more new methods have been proposed in recent years. In this paper, we propose a novel face recognition method which is based on PCA and logistic regression. PCA is one of the most important methods in pattern recognition. Therefore, in our method, PCA is used to extract feature and reduce the dimensions of process data. Afterwards, we present a novel classification algorithm and use logistic regression as the classifier for face recognition. The experimental results on two different face databases are presented to illustrate the efficacy of our proposed method.     

一种基于样本生成的人脸识别方法

人脸识别问题的特点包括样本的特征维数高和每个类别所包含的样本较少。设计有效的特征提取方法是解决人脸识别问题的关键要素之一。提出了在采用降采样获得特征的同时利用新的降采样方法多次对原图片进行降采样,生成多幅训练样本,进而缓解人脸识别中的小样本问题。实验结果证明所提出的方法能有效地提高分类器的精度。