基于曲波变换和余弦测度的人脸识别方法

人脸识别是一种常用的生物特征识别技术,广泛应用于门禁考勤、公安司法等领域。光照、人脸表情与姿态、遮挡等采集条件的变化对 现有人脸识别方法 影响较大,限制了其应用。提出了一种基于曲波变换和余弦测度的人脸识别方法,以提高人脸识别对采集条件的鲁棒性。首先,对待识别人脸图像进行曲波变换,依据曲波系数检测人脸区域的关键点;然后,提取各关键点在不同尺度和方向上的曲波特征,构建人脸特征描述子;最后,依据余弦测度、累加和运算和极值运算求取人脸的最优匹配结果。仿真实验表明,所提方法对光照、姿态、表情和遮挡等变化的鲁棒性强,且识别性能好。     

小样本条件下基于随机森林和Haar特征的多姿态人脸识别

针对小样本环境下存在人脸姿态、表情变化等干扰时的人脸识别问题,提出利用基于Haar特征的随机森林分类器完成对注册样本和待识别人脸图像的关键点自适应定位,再以SURF(Speed-Up Robust Features)特征的欧氏距离决策得出初匹配和再匹配关键点,完成人脸识别,解决在小样本环境下识别多姿态人脸图像的问题。实验结果证明,该方法在表情、姿态变化等干扰情况下能有效提高小样本人脸识别的识别率。     

小规模数据多角度人脸表情人工智能识别仿真

已有人脸表情智能识别方法存在图像遗漏、识别速度慢、以及表情易混淆的问题，导致识别正确率低，且不适用于多人脸识别。为解决上述问题，提出小规模数据多角度人脸表情人工智能识别方法。结合去噪自编码器、稀疏自编码器及普通自编码器组成具有5层网络结构的堆栈式混合自编码器。在网络结构中训练数据样本，并对样本完成微调、权重初始化和更新。再结合粒子群优化分类器识别表情，将粒子最优值带入人脸表情识别目标函数，获得判定表情的决策矩阵，完成人脸表情人工智能识别。仿真结果表明，所提方法识别多人表情识别时无遗漏，且识别速度快、精度高、人脸表情不易混淆。     

基于立体视觉的三维人脸识别

1.引言 近年来生物特征在安全领域的应用越来越受到重视,人脸识别即为其中一个重要分支.迄今为止,人们已经提出了多种人脸识别的算法[1,2],大多数算法是针对二维图像设计的,具有识别速度快、方法直观的特点.存在的问题是很难克服光照、背景及表情等因素的干扰.本文尝试通过人脸的三维数据匹配进行人脸识别,以提高识别的可靠性.由于实验设备、速度及特征提取等多方面因素的影响,关于人脸三维识别的研究工作相对较少.Gordon[3]利用人脸深度图计算出人脸表面曲线的曲率分布并进行分析,得到人脸各个器官的位置及相关几何特征;Beumier和Acheroy[4]通过对人脸投影结构光提取人脸的三维信息,将人脸侧面轮廓曲线与人脸中线附近区域的灰度分布相结合作为识别特征.我们利用人脸的三维稠密数据同人脸三维模型进行匹配来实现人脸的识别,取得了满意的效果.     

符合认知心理的三维人脸线条化评估方法

针对三维人脸几种典型的特征线条表达类型,结合人脸的领域知识和人脸识别认知机理,面向人脸线条化艺术方式,提出一种适用于人脸识别认知机理的人脸表情、朝向和五官的主观识别和评估方法.首先对大量的用户人脸进行识别测试,统计不同风格化线条在识别过程的准确率和效率,评估在不同表情和人脸区域中不同线条的表达能力;然后依据评估结论融合不同类型线条,生成人脸线条化优化绘制结果.实验结果表明,优化后的线条化结果更加有利于人脸的主观识别,该方法可推广至人脸肖像画、油画等多种风格,适用于人脸肖像具有象征主义的艺术表达形式.     

一种混合特征的人脸识别算法仿真研究

研究人脸识别优化问题,人脸图像受光照、人脸表情和位置变化等因素影响,由于图像具有复杂的多尺度特征,传统人脸识别算法只能提提取局部或全局特征,不能准确描述人脸图像,导致人脸识别率低。为了提高人脸识别率,提出一种小波分解和LBP算子相结合的人脸识别算法(WTLBP)。WTLBP首先利用小波变换对人脸图像进行分解,将人脸图像分解成大尺度和小尺度图像,然后采用LBP算子提取人脸图像的多尺度特征,最后采用概率统计法对人脸进行匹配识别。对ORL人脸库进行仿真,结果表明,WTLBP能够提取到人脸图像更加丰富的局部和全局信息,对光照、人脸表情和位置变化具有较高的鲁棒性,提高了人脸识别率。     

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。     

基于改进几何特征的三维人脸识别方法

研究人脸识别问题,当人脸的表情发生很大变化时,提取的三维几何特征会发生较大变化,造成识别准确度很低,影响识别效果.为解决上述问题,提出了一种改进几何特征的三维人脸识别方法.通过求出人脸几何特征差异的关联程度,计算哪种三维几何特征变化与识别结果是无关的,哪种与识别结果足关联的,解决了人脸表情变化时的准确三维识别问题进行仿真.结果表明,改进方法实现识别高效,可使得三维人脸识别的准确度大大的提高,为设计提供了支持.     

基于人脸图像的质量评价模型及应用

提出一种人脸图像评价模型。利用该模型评估人脸图像光照、姿态、表情、污损等噪声系数,进而选择噪声系数较低的图像用于人脸识别,提高人脸识别率。选择在CAS-PEAL人脸数据库作为实验对象,根据人脸图像质量评价模型选择噪声系数较低的人脸图像构成第一数据库。实验结果表明,根据提出的模型评价噪声系数并选择噪声系数低的图像用于识别,人脸识别性能得到改善。     

基于因素分解模型的两步人脸识别

为了减轻人脸识别中表情以及姿态等因素变化对识别结果的影响,Xu提出了利用原始样本和对称样本的两步人脸识别算法。但当人脸图像受外在因素干扰产生较大变化时,该方法的识别结果并不理想。因此提出了一种基于因素分解模型的两步人脸识别算法。新算法在特征提取过程中利用因素分解模型将“身份因素”和“表情因素”从人脸图像中分离出来,加以控制。然后提取测试集图像中的新身份和新表情,并将其与训练集中的旧身份或旧表情相互作用,合成新的人脸图像。同时为了保证分类精度,在识别阶段针对原始样本和合成样本分别采用两步人脸识别的方法,充分利用了分数层次融合的优势,进一步提高了算法的识别效果。     

Joint prototype and metric learning for image set classification: Application to video face identification

In this paper, we address the problem of image set classification, where each set contains a different number of images acquired from the same subject. In most of the existing literature, each image set is modeled using all its available samples. As a result, the corresponding time and storage costs are high. To address this problem, we propose a joint prototype and metric learning approach. The prototypes are learned to represent each gallery image set using fewer samples without affecting the recognition performance. A Mahalanobis metric is learned simultaneously to measure the similarity between sets more accurately. In particular, each gallery set is represented as a regularized affine hull spanned by the learned prototypes. The set-to-set distance is optimized via updating the prototypes and the Mahalanobis metric in an alternating manner. To highlight the importance of representing image sets using fewer samples, we analyzed the corresponding test time complexity with respect to the number of images used per set. Experimental results using YouTube Celebrity, YouTube Faces, and ETH-80 datasets illustrate the efficiency on the task of video face recognition, and object categorization.     

Real-Time Pose-Invariant Face Recognition Using the Efficient Second-Order Minimization and the Pose Transforming Matrix

We propose a real-time pose-invariant face recognition algorithm from a gallery of frontal images only. (i) We modified the second-order minimization method for the active appearance model (AAM). This allows the AAM to have the ability of correct convergence with little loss of frame rate. (ii) We proposed a pose transforming matrix that can eliminate warping artifacts of the warped face image from AAM fitting. This makes it possible to train a neural network as the face recognizer with one frontal face image of each person in the gallery set. (iii) We propose a simple method for pose recognition by using neural networks to select the proper pose transforming matrix. The proposed algorithm was evaluated on a set of 2000 facial images of 10 people (200 images for each person obtained at various poses), achieving a great improvement in recognition.     

一种鲁棒稀疏表示的单样本人脸识别算法

稀疏表示在人脸识别问题上取得了非常优秀的识别结果,但在单样本条件下,算法性能下降严重。为提高单样本条件下稀疏表示的应用能力,提出一种鲁棒稀疏表示单样本人脸识别算法(RSR)。通过使用每张人脸图像创建一组位置图像,扩充每个对象训练样本,并利用L2,1范数约束,保证RSR选择正确对象的位置图像。在AR和Extended Yale B人脸数据库上进行评测,实验结果表明RSR能够有效处理存在遮挡或光照变化的人脸图像,获得了较好的单样本人脸识别准确率,具有很强的鲁棒性。     

Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios.     

基于改进的多流形算法的人脸图像集识别

一个图像集由大量变化不一的图像组成,而且这些图像都表示同一个人.现实中的图像集数据是非线性的,造成这些现象的因素有人脸的角度不同、光线的明暗等,因此图像集中的每幅图像都是变化的,如果近似的将一个图像集建模为线性子空间,而忽略了集合中数据结构的变化,很显然是不合理的,这也必然会影响到最后的识别率.受流形理论知识的启发,可以将图像集建模为一个流形,这与传统的将图像集建模为子空间的方法有着本质区别.本文在基于流形的人脸图像集识别方法的基础上进行改进,提出新的计算样子空间距离方法,最后采用所有最短子空间距离的平均值作为流形之间的距离,称为改进的多流形方法(Improved multi-manifold method,IMM).IMM方法在CMU PIE数据库上进行实验,结果表明该方法相比其他方法具有更高识别率.     

Orthogonal discriminant vector for face recognition across pose

Recognizing face images across pose is one of the challenging tasks for reliable face recognition. This paper presents a new method to tackle this challenge based on orthogonal discriminant vector (ODV). The result of our theoretical analysis shows that an individual’s probe image captured with a new pose can be represented by a linear combination of his/her gallery images. Based on this observation, in contrast to the conventional methods which model face images of different individuals on a single manifold, we propose to model face images of different individuals on different linear manifolds. The contribution of our approach includes: (1) to prove that the orthogonality to ODVs is a pose-invariant feature.; (2) to categorize each person with a set of ODVs, where his/her face images posses zero projections while other persons’ images are characterized by maximum projections; (3) to define a metric to measure the distance between a face image and an ODV, and classify the face images based on this metric. Our experimental results validate the feasibility of modeling the face images of different individuals on different linear manifolds. The proposed method achieves higher accuracy on face recognition and verification than the existing techniques.     

视频人脸识别中判别性联合多流形分析

将基于视频的人脸识别转换为图像集识别问题,并提出两种流形来表示每个图像集:一种是类间流形,表示每个图像集的平均脸信息;另一种是类内流形,表示每个图像集的所有原始图像的信息.类间流形针对图像集之间的区别提取整体判别信息,作用是选出几个与待识别图像集较为相似的候选图像集.类内流形则考虑图像集内各原始图像之间的关系,负责从候选图像集中找出最为相似的一个.不同于现有的非线性流形方法中每幅图像对应流形中的一个点,采用分片技术学习两种流形的投影矩阵,每个分片对应流形中的一个点,所学到的特征更具有判别性,进而使流形边界更加清晰,同时解决了传统非线性流形方法中的角度偏差和不充分采样问题.还提出了与分片技术相匹配的流形之间的距离度量方法.最后在几个广为研究的数据集上进行了实验,结果表明:新方法的识别准确率高,尤其适用于不受控环境下的视频识别,而且不受视频段长短的影响.     

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

Discriminative transfer learning with sparsity regularization for single-sample face recognition

Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario. In this paper, we present a new discriminative transfer learning (DTL) approach for SSFR, where discriminant analysis is performed on a multiple-sample generic training set and then transferred into the single-sample gallery set. Specifically, our DTL learns a feature projection to minimize the intra-class variation and maximize the inter-class variation of samples in the training set, and minimize the difference between the generic training set and the gallery set, simultaneously. To make the DTL be robust to outliers and noise, we employ a sparsity regularizer to regularize the DTL and further propose a novel discriminative transfer learning with sparsity regularization (DTLSR) method. Experimental results on three face datasets including the FERET, CAS-PEAL-R1, and real-world LFW datasets are presented to show the efficacy of the proposed methods.