复杂光照条件下的多姿状鲁棒性人脸识别方法

为改善复杂光照条件下的多姿状鲁棒性人脸识别的效果,提出了小波变换与LBP的多姿状鲁棒性人脸识别方法.通过二维离散小波变换对人脸图像进行二级小波分解提取到低频特征信息分量,并以重构初始图像的方式实现降噪滤波处理,滤除低频光照分量后完成复杂光照补偿;继续分解复杂光照补偿后的图像,采用LBP算子对子图像的鲁棒性部分纹理特征进...     

An LBP-based multi-scale illumination preprocessing method for face recognition

It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry re...     

一种消除光照影响的人脸识别方法

光照变化容易使人脸图像的灰度分布不均匀,造成局部对比度差别较大,影响人脸识别的效果。为此本文首先分别对人脸图像进行直方图均衡化和对数变换,接着将处理后的图像进行融合,然后运用PCA算法对人脸图像进行特征提取,最后采用三阶近邻分类法来实现人脸识别。通过对Yale、ORL和FERET人脸库的仿真实验结果表明,该方法在人脸图像光照变化的情况下,能够较好地改善人脸补偿的效果,具有较高的平均识别正确率。     

On the use of phase of the Fourier transform for face recognition under variations in illumination

In this paper, we propose a representation of the face image based on the phase of the 2-D Fourier transform of the image to overcome the adverse effect of illumination. The phase of the Fourier transform preserves the locations of the edges of a given face image. The main problem in the use of the phase spectrum is the need for unwrapping of the phase. The problem of unwrapping is avoided by considering two functions of the phase spectrum rather than the phase directly. Each of these functions gives partial evidence of the given face image. The effect of noise is reduced by using the first few eigenvectors of the eigenanalysis on the two phase functions separately. Experimental results on combining the evidences from the two phase functions show that the proposed method provides an alternative representation of the face images for dealing with the issue of illumination in face recognition.     

Supervised within-class-similar discriminative dictionary learning for face recognition

The current study puts forward a supervised within-class-similar discriminative dictionary learning (SCDDL) algorithm for face recognition. Some popular discriminative dictionary learning schemes for recognition tasks always incorporate the linear classification error term into the objective function or make some discriminative restrictions on representation coefficients. In the presented SCDDL algorithm, we propose to directly restrict the representation coefficients to be similar within the same class and simultaneously include the linear classification error term in the supervised dictionary learning scheme to derive a more discriminative dictionary for face recognition. The experimental results on three large well-known face databases suggest that our approach can enhance the fisher ratio of representation coefficients when compared with several dictionary learning algorithms that incorporate linear classifiers. In addition, the learned discriminative dictionary, the large fisher ratio of representation coefficients and the simultaneously learned classifier can improve the recognition rate compared with some state-of-the-art dictionary learning algorithms.     

具有学习功能的自动人脸识别

人脸识别是模式识别领域中一个相当困难而又有理论意义和实际价值的研究课题。传统的基于K－L变换的自动人脸识别方法,不用过多地考虑人脸的局部特征,利用特征脸方法进行识别,取得了一定的。但是,人脸作为一个特殊的场景,脸像会受年龄、心情、拍摄角度、光照条件、发饰等因素影响,所成图像存在差异。传统的基于K－L变换的自动人脸识别方法不能很好地克服这些畸变的影响。文中就主成分分析方法引入人脸识别,模拟人脸脸像的各种变化,事先对脸像做相应的变化,产生一系列变形脸。然后对变形脸进行主成分分析,提取它们的主成分。最后应用遗传算法选择最优特征向量构造子空间,提出一种能抗御一定脸像变化的人脸识别方法,并运用该方法进行了实验。实验结果证明了该方法的可行性和良好的抗畸变能力。     

Automatic lip localization under face illumination with shadow consideration

Lip-reading has potential attractive applications in information security, speech recognition, secret communication and so forth. To build an automatic lip-reading system, one key issue is how to locate the lip region, particularly under the changing illumination condition. Empirical studies have shown that the recognition rate of a lip-reading system greatly relies on the accuracy of the lip localization. Unfortunately, to the best of our knowledge, lip localization under face illumination with shadow consideration has not been well solved yet. Moreover, this problem is also one of the major obstacles to keeping an automatic lip-reading system from the practical applications. This paper therefore concentrates on this problem and proposes a new approach to obtain the minimum enclosing rectangle surround of a mouth automatically based upon the transformed gray-level image. In this approach, a pre-processing is firstly made to reduce the interference caused by shadow and enhance the boundary region of lip, through which the left and right mouth corners are estimated. Then, by building a binary sequence based on the gray-level values along with the vertical midline of mouth, the top and bottom crucial points can be estimated. Experiments show the promising result of the proposed approach in comparison with the existing methods.     

小波核极限学习机在人脸识别中的应用

文章提出了一种基于小波核极限学习机(Wavelet Kernel Extreme Learning Machine,WK-ELM)的人脸识别算法。首先,使用2D盖博小波变换对人脸图片进行初步的人脸特征提取。为了从所有提取的特征中选择出与人脸识别相关的、必要的特征,使用主成分分析法(Principal Component Analysis,PCA)对经过初步处理后的图像再进行进一步处理,有效地降低了特征维数。然后使用小波核极限学习机对提取到的图像进行分类。实验证明,小波核极限学习机不仅识别性能高,而且训练速度也优于其他算法。     

An appearance model constructed on 3-D surface for robust face recognition against pose and illumination variations

We propose a face recognition method that is robust against image variations due to arbitrary lighting and a large extent of pose variations, ranging from frontal to profile views. Existing appearance models defined on image planes are not applicable for such pose variations that cause occlusions and changes of silhouette. In contrast, our method constructs an appearance model of a three-dimensional (3-D) object on its surface. Our proposed model consists of a 3-D shape and geodesic illumination bases (GIBs). GIBs can describe the irradiances of an object's surface under any illumination and generate illumination subspace that can describe illumination variations of an image in an arbitrary pose. Our appearance model is automatically aligned to the target image by pose optimization based on a rough pose, and the residual error of this model fitting is used as the recognition score. We tested the recognition performance of our method with an extensive database that includes 14 000 images of 200 individuals with drastic illumination changes and pose variations up to 60/spl deg/ sideward and 45/spl deg/ upward. The method achieved a first-choice success ratio of 94.2% without knowing precise poses a priori.     

Background learning for robust face recognition with PCA in the presence of clutter.

We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.     

结合深度Q学习和注意模型的视频人脸识别

针对视频人脸识别中存在的动态人脸信息捕捉困难和局部人脸特征提取粗糙的问题,提出了一种基于深度Q学习和注意模型结合的视频人脸识别方法。首先,采用卷积神经网络(Convolutional Neural Network,CNN)训练视频数据可提取多维特征;其次,将视频特征输入注意模型,根据视频数据时间连续性信息得到局部人脸特征、人脸位置和时间记忆单元;最后,采用Q学习迭代计算注意模型的输出,找到含人脸的最优帧序列,并以此计算视频匹配准确度。实验结果表明,该方法有效提高了复杂背景下视频人脸识别的准确性。     

字典学习优化结合HMAX模型的鲁棒人脸识别

针对人脸识别中由于姿态、光照等变化而影响识别性能的问题,提出了字典学习优化结合HMAX模型的人脸识别方法。首先,使用样本图像和从样本获得的仿射包模型联合表示一幅图像;然后,利用HMAX模型提取C2特征,并利用字典学习优化特征矩阵;最后,将视觉注意模型与原始模型的C2特征进行组合,并利用支持向量机完成分类。在Caltech和AR人脸数据库上的实验结果表明,相比其他几种较新的人脸识别方法,提出的方法取得了更好的识别性能,对人脸表情和光照变化具有鲁棒性。     

Face recognition under large illumination variations using homomorphic filtering in spatial domain

This paper proposes a homomorphic filtering in spatial domain for reducing of illumination effects in face recognition systems. Also, in this research a simple kernel of homomorphic filter is proposed. Application of this method causes considerable reduction in computational time in the preprocessing step. When a new face image with an arbitrary illumination is given, the homomorphic filter is applied and its reflectance component is extracted. Then the reflectance component is divided into several local regions and histograms of each local region are extracted using multi-resolution uniform local Gabor binary patterns (MULGBP). These histograms are combined for obtaining the overall histogram of the images. Finally, for face recognition, a simple histogram matching process is performed between new face image histogram and the gallery images histogram. The results show that the proposed method is robust for large illumination variation with a reasonable computational complexity.     

Orthogonal laplacianfaces for face recognition.

Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm.     

DWT based HMM for face recognition

A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.     

Orthogonal isometric projection for face recognition

Isometric projection(IsoProjection) is a linear dimensionality reduction method,which explicitly takes into account the manifold structure embedded in the data.However,IsoProjection is non-orthogonal,which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality.The non-orthogonality also distorts the metric structure embedded in the data.This paper proposes a new method called orthogonal isometric projection(O-IsoProjection),which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection.Similar to IsoProjection,O-IsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space,and then obtains the projections by preserving such a graph structure.Different from IsoProjection,O-IsoProjection requires the basis vectors to be orthogonal,and the orthogonal basis vectors can be calculated by iterative way.Experimental results on ORL and Yale databases show that O-IsoProjection has better recognition rate for face recognition than Eigenface,Fisherface and IsoProjection.     

基于判别邻域嵌入的人脸识别

提出了一种人脸识别子空间方法:判别邻域嵌入(DNE).在框架中,训练样本数据的邻域和类关系被用来构建低维嵌入流形.在嵌入低维子空间后,同类样本保持它们固有的邻域关系,相反不同类近邻样本彼此远离.在ORL和Yale人脸数据库上,对提出的方法和主成分分析(PCA)、线性判别分析(LDA)、保持邻域嵌入(NPE)和保持局部投影(LPP)方法进行了比较,结果表明,提出的方法是有效的.     

人脸图像检测与正规化的研究

人脸图像检测在实际运行中,复杂的背景、和人脸选择位置与大小都会影响到人脸识别率。为了克服这些因素并减少环境的干扰,设计了一种人脸图像检测与正规化的方法,详细论述了实现的关键技术。通过实验表明,人脸图像识别率和速度都有很大的改善,满足了实际环境中实时处理的需要。