Band-pass correlation filter for illumination- and noise-tolerant face recognition

The paper proposes a band-pass correlation filter in frequency domain for frontal face recognition task under both poor illumination and noisy condition. The band-pass nature of the proposed filter is achieved through combination of a modified high-pass filter and a continuous wavelet filter. An optimal range of scale is selected for this wavelet filter. The performance of the proposed band-pass correlation filter for face recognition tasks under variations in illumination and noise is evaluated and compared with other filters using standard databases (YaleB and PIE). High recognition accuracy is achieved in this proposed technique.     

An efficient mesh router nodes placement in wireless mesh networks based on moth-flame optimization algorithm

One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi-objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.     

多姿态、光照不均人脸图像的子空间人脸识别方法研究

针对人脸识别中存在的光照不均匀问题,提出了一种预处理链技术,能达到很好的光照补偿效果。为了提高多姿态、多表情、多细节人脸图像的人脸识别率,设计了一种将最近邻分类器与支持向量机相结合的分类算法（NN-SVM）,基于该分类算法提出了一种基于Gabor变换和NN-SVM的子空间人脸识别方法。在FERET和ORL两大人脸数据库中对所提方法进行性能评估,实验结果表明所提出方法能有效的解决人脸识别中光照不均匀问题,大大的提高人脸识别率,而且相比其他现存的人脸识别方法,所设计的方法具有更好、更稳定的识别效果。     

Matching pursuit filters applied to face identification

We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuit filters. A matching pursuit filter is an adapted wavelet expansion, where the expansion is adapted to both the data and the pattern recognition problem being addressed. For identification, the filters find the features that differentiate among faces, whereas, for detection, the filters encode the similarities among faces. The filters are designed though a simultaneous decomposition of a training set into a two-dimensional (2-D) wavelet expansion. This yields a representation that is explicitly 2-D and encodes information locally. The algorithm uses coarse to fine processing to locate a small set of key facial features, which are restricted to the nose and eye regions of the Face. The result is an algorithm that is robust to variations in facial expression, hair style, and the surrounding environment. Based on the locations of the facial features, the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification. The algorithm was demonstrated on three sets of images. The first set was images from the FERET data base. The second set was infrared and visible images of the same people. This demonstration was done to compare performance on infrared and visible images individually, and on fusing the results from both modalities. The third set was mugshot data from a law enforcement application.     

一种用于细粒度人脸识别的眼镜去除方法

为解决眼镜遮挡会降低人脸识别性能的难点,借鉴深度卷积神经网络在超分辨率方面的成功应用,该文提出一种用于细粒度人脸识别的眼镜自动去除方法ERCNN.用卷积层、池化层、MFM特征选取模块和反卷积层设计ERCNN网络模型,自动学习戴眼镜和未戴眼镜人脸图像对之间的映射关系,实现端到端的眼镜去除.然后,收集大量监控场景下的人脸图像,以及互联网上公开的人脸图像作为训练集;同时构建SLLFW数据集,作为眼镜去除和人脸识别的测试集.最后,通过与传统的眼镜去除方法进行对比试验,该文算法的各项评价指标优于传统方法,能有效的去除真实人脸图像中眼镜;同时在SLLFW人脸数据集上形成的全框眼镜、半框眼镜和无框眼镜人脸数据集上对多种人脸识别算法进行对比试验.试验表明,在FAR为1%的情况下,利用该文方法对F-SLLFW, H-SLLFW和R-SLLFW数据集的人脸图像进行眼镜去除后,SphereFace算法的TAR分别达到90.05%, 91.14%和92.33%,比未去除眼镜的识别率分别提高了3.92%, 3.08%和1.26%;同样,在FAR为0.1%的情况下,比SphereFace算法的TAR分别提高了10.06%, 4.29%和2.13%,说明该文方法有助于提升细粒度人脸识别的识别精度.     

A Multi-Condition Relighting with Optimal Feature Selection to Robust Face Recognition with Illumination Variation

In this paper, we proposed a new approach for face recognition with robust to illumination variation. The improved performance to various lights in recognition is obtained by a novel combination of multi- condition relighting and optimal feature selection. Multi-condition relighting provides a ＂coarse＂ compensation for the variable illumination, and then the optimal feature selection further refines the compensation, and additionally offers the robustness to shadow and highlight, by deemphasizing the local mismatches caused by imprecise lighting compensation, shadow or highlight on recognition. For evaluation, two databases with various illumination mismatches have been used. The results have demonstrated the improved robustness of the new methods.     

Facial Recognition Using Multisensor Images Based on Localized Kernel Eigen Spaces

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.      

Gabor feature based classification using the enhanced fisher lineardiscriminant model for face recognition

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from (1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; (2) the development of a Gabor-Fisher classifier for multi-class problems; and (3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.     

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...     

融合局部语义与全局信息的人脸表情识别

人脸表情识别在人机交互等人工智能领域发挥着 重要作用,当前研究忽略了人脸的语 义信息。本 文提出了一种融合局部语义与全局信息的人脸表情识别网络,由两个分支组成:局部语义区 域提取分支 和局部-全局特征融合分支。首先利用人脸解析数据集训练语义分割网络得到人脸语义解析 ,通过迁移训 练的方法得到人脸表情数据集的语义解析。在语义解析中获取对表情识别有意义的区域及其 语义特征, 并将局部语义特征与全局特征融合,构造语义局部特征。最后,融合语义局部特征与全局特 征构成人脸 表情的全局语义复合特征,并通过分类器分为7种基础表情之一。本文同时提出了解冻部分 层训练策略, 该训练策略使语义特征更适用于表情识别,减 少语义信息冗余性。在两个公开数据集JAFFE 和KDEF上 的平均识别准确率分别达到了93.81%和88.78% ,表现优于目前的深度学习方法和传统方法。实验结果证 明了本文提出的融合局部语义和全局信息的网络能够很好地描述表情信息。     

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。      

改进投影梯度非负矩阵分解的单训练样本特征提取研究

人脸识别是当前人工智能和模式识别的研究热点。非负矩阵分解(NMF)能够反映样本的局部的内在的联系,可用于单样本特征提取,但时间复杂度较高。投影梯度(Projected Gradient,PG)优化方法大幅降低了NMF约束优化迭代问题的时间复杂度,但是单训练样本存在对本类信息量描述不足的缺点。为此,该文提出了一种基于改进的投影梯度非负矩阵分解 (Improved Projected Gradient Non-negative Matrix Factorization,IPGNMF) 的单训练样本特征提取方法。在进行PGNMF算子之前,先将训练样本作Gabor分解,分解后的Gabor子图像在各个方向上可以更加丰富的描述样本特征,最后将各个Gabor子图像的PGNMF特征进行融合,作为最终的识别特征。在对人脸库ORL,YEL与FERET的识别实验中,与经典的特征提取方法比较,证明了可以有效地解决单训练样本人脸识别的问题。     

Entropy-based feature selection for improved 3D facial expression recognition

Facial expressions contain most of the information on human face which is essential for human–computer interaction. Development of robust algorithms for automatic recognition of facial expressions with high recognition rates has been a challenge for the last 10 years. In this paper, we propose a novel feature selection procedure which recognizes basic facial expressions with high recognition rates by utilizing three-Dimensional (3D) geometrical facial feature positions. The paper presents a system of classifying expressions in one of the six basic emotional categories which are anger, disgust, fear, happiness, sadness, and surprise. The paper contributes on feature selections for each expression independently and achieves high recognition rates with the proposed geometric facial features selected for each expression. The novel feature selection procedure is entropy based, and it is employed independently for each of the six basic expressions. The system’s performance is evaluated using the 3D facial expression database, BU-3DFE. Experimental results show that the proposed method outperforms the latest methods reported in the literature.     

A comparative study of local matching approach for face recognition.

In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The conclusions from our experiments include: (1) additional evidence that Gabor features are effective local feature representations and are robust to illumination changes; (2) discrimination based only on a small portion of the face area is surprisingly good; (3) the configuration of facial components does contain rich discriminating information and comparing corresponding local regions utilizes shape features more effectively than comparing corresponding facial components; (4) spatial multiresolution analysis leads to better classification performance; (5) combining local regions with Borda count classifier combination method alleviates the curse of dimensionality. We implemented a complete face recognition system by integrating the best option of each step. Without training, illumination compensation and without any parameter tuning, it achieves superior performance on every category of the FERET test: near perfect classification accuracy (99.5%) on pictures taken on the same day regardless of indoor illumination variations, and significantly better than any other reported performance on pictures taken several days to more than a year apart. The most significant experiments were repeated on the AR database, with similar results.     

光照健壮人脸识别的低秩相对梯度直方图特征

为了进一步提升人脸梯度特征的光照健壮性,本文结合低秩分解能有效分离图像本质特征和噪声的特性,提出了一种光照健壮的低秩相对梯度直方图特征提取方法。首先,通过对人脸图像进行相对梯度运算获得了图像的相对梯度幅值图像和各像素的梯度方向信息。然后,为了去除相对梯度图像中由于非均匀光照而引入的光照边缘误差,利用低秩分解将相对梯度图像分解为低秩分量和稀疏噪声分量之和。最后,结合人脸图像的梯度方向信息对相对梯度图像的低秩分量进行离散化、滤波和局部二值模式编码形成了人脸的低秩相对梯度直方图特征。在经典的FE-RET子集以及具有代表性的YaleB和PIE光照子集上的实验显示：低秩相对梯度直方图特征的人脸识别性能显著优于相对梯度直方图特征、方向梯度幅值模式特征和图像低秩特征等方法的性能;在YaleB子集上,低秩相对梯度直方图特征的人脸识别精度比相对梯度直方图特征的人脸识别精度高至少4％。实验结果证明,低秩相对梯度直方图特征对光照变化,尤其是非均匀光照变化的人脸识别具有很强的健壮性。     

NSCT域自适应人脸图像光照不变特征提取

为了减少光照变化对人脸识别算法的影响,提出了一种基于非下采样Contourlet变换(NSCT,nonsubsampled contourlet transform)的光照不变特征提取方法。人脸图像经过对数变换(LT)后,利用NSCT进行分解,得到图像的低频子带和高频方向子带;根据高频子带中NSCT系数的概率分布,给出各子带的自适应阈值,并采用折衷阈值函数进行滤波;对滤波后的子带进行NSCT逆变换,得到人脸图像的光照不变特征。在Extended Yale B和CMU PIE人脸数据库上的实验结果表明,本文方法能有效减少光照影响,显著提高了识别率。     

基于多尺度训练库与多特征融合的人脸识别

针对光照差异、表情变化、遮挡等因素造成人脸识别率低的问题,提出一种基于多尺度训练库和加权特征的鲁棒性人脸识别算法。首先根据不同大小的图片具有不同信息量的特点定义并建立多尺度训练库,然后采用RPCA方法对人脸图像进行分解,之后进行HMLBP特征和Eigenface特征提取,最后引入一个权重因子将两种特征进行加权融合,并采用基于稀疏表达的方法对人脸图像进行识别。实验结果表明,相比其他人脸识别算法,本文提出的算法对标准人脸库保持较高识别率,最高可达99%,同时对遮挡人脸库也具有较好的识别效果,鲁棒性较高。     

基于稀疏表示的人脸姿态估计研究

针对人脸光照、遮挡、身份、表情等因素变化的人脸姿态估计难题,结合稀疏表示分类(SRC)方法的优秀识别性能,对SRC理论进行了深入分析,并将其应用于人脸姿态分类.为了解决姿态估计中人脸光照、噪声和遮挡变化问题,将人脸姿态离散化为不同的子空间,每个子空间对应一个类别,据此,提出基于字典学习与稀疏约束的人脸姿态识别方法.通过在公开的XJTU和PIE人脸库上实验表明:所研究的方法对人脸光照、噪声和遮挡变化具有鲁棒性.     

基于二维Fisher线性判别的人脸耳组合识别

针对人脸易受到年龄、表情等影响,提出了脸和耳相结合的组合识别方法。利用二维Fisher线性判别(2DFLD)方法分别进行了脸、耳图像层和特征层的组合识别。在北京科技大学人耳库和ORL人脸库上进行实验,结果表明,图像层组合和特征层组合的识别率分别为97.5%、95.0%,分别比人脸识别提高了12.5%和10.0%,比人耳识别提高了5.0%和2.5%;与同样应用于组合识别的主成分分析(PCA)、二维PCA(2DPCA)比较,也取得了较好识别效果。这说明,多生物特征组合识别是一种有效的识别方法。