2D face fitting-assisted 3D face reconstruction for pose-robust face recognition

Recent face recognition algorithm can achieve high accuracy when the tested face samples are frontal. However, when the face pose changes largely, the performance of existing methods drop drastically. Efforts on pose-robust face recognition are highly desirable, especially when each face class has only one frontal training sample. In this study, we propose a 2D face fitting-assisted 3D face reconstruction algorithm that aims at recognizing faces of different poses when each face class has only one frontal training sample. For each frontal training sample, a 3D face is reconstructed by optimizing the parameters of 3D morphable model (3DMM). By rotating the reconstructed 3D face to different views, pose virtual face images are generated to enlarge the training set of face recognition. Different from the conventional 3D face reconstruction methods, the proposed algorithm utilizes automatic 2D face fitting to assist 3D face reconstruction. We automatically locate 88 sparse points of the frontal face by 2D face-fitting algorithm. Such 2D face-fitting algorithm is so-called Random Forest Embedded Active Shape Model, which embeds random forest learning into the framework of Active Shape Model. Results of 2D face fitting are added to the 3D face reconstruction objective function as shape constraints. The optimization objective energy function takes not only image intensity, but also 2D fitting results into account. Shape and texture parameters of 3DMM are thus estimated by fitting the 3DMM to the 2D frontal face sample, which is a non-linear optimization problem. We experiment the proposed method on the publicly available CMUPIE database, which includes faces viewed from 11 different poses, and the results show that the proposed method is effective and the face recognition results toward pose variants are promising.     

一个人脸识别系统的设计与实现

设计了一个在区分人脸朝向的基础上，再进行人脸辨别的识别系统。把BP网络用于人脸朝向的识别，并且用一组单输出的子网络代替原来的BP网络，提高了人脸朝向的识别率。在人脸朝向的分类基础上，用主元分析法进行人脸识别。实验结果表明此系统提高了人脸的识别率。     

人脸识别方法综述*

对一系列人脸识别方法进行了综合性描述。首先介绍了人脸识别的概念及其发展历史,指出了人脸识别所面临的主要困难;随后对人脸识别技术方法发展过程中一些经典的流行的方法进行了比较详细的阐述。最后介绍了人脸识别技术在国内外的应用状况。     

红外图像人脸识别方法研究进展

介绍了红外图像人脸识别应用前景和困难所在,阐述了红外图像人脸识别的特征,报告了红外图像人脸识别近年来的研究进展,综述了红外图像人脸识别的方法和技术,介绍了基于红外频谱的人脸和伪装检测方法、基于热红外成像的人脸识别方法、基于支撑向量机的红外图像人脸识别方法,基于线性辨别分析的红外图像人脸识别新方法,并进行了简单的分析和比较.最后探讨了红外图像人脸识别领域的发展与研究方向.     

改进人脸特征矫正网络的遮挡人脸识别方法

现有人脸识别模型受口罩等遮挡因素影响导致准确率无法提升。当前主流研究方法将有无遮挡场景分开训练后,整合应用于多场景。针对遮挡人脸识别模型的局限性,提出一种改进人脸特征矫正网络（FFR-Net）模型。该模型可同时用于有无遮挡人脸识别并应用于口罩与眼镜遮挡两种识别场景中。人脸特征矫正网络模型提出了一种人脸特征矫正模块,为保证充分利用无遮挡区域特征信息,在该模块中的空间分支引入involution算子扩大图像信息交互区域,增强在空间范围内面部特征信息;在通道分支引入坐标注意力机制,捕获跨通道信息以增强特征表示,利于模型准确地定位识别目标区域;将Meta-ACON作为该模块新的动态激活函数,通过动态调整线性或非线性程度以提高模型泛化能力和计算准确度。最后,利用改进的人脸特征矫正网络模型在CASIA-Webface经处理的有无口罩遮挡人脸数据集上进行训练,其在LFW经处理的有无口罩遮挡数据集、Meglass数据集上的测试结果准确率分别达到了82.50%和89.75%,优于现有算法,验证了所提方法的有效性。     

Hyperspectral-cube-based mobile face recognition: A comprehensive review

With the hyperspectral sensor technology evolving and becoming more cost-effective, hyperspectral imaging offers new opportunities for robust face recognition. Hyperspectral face cubes contain much more spectral information than face images from common RGB color cameras. Hyperspectral face recognition is robust to the impacts, such as illumination, pose, occlusion, and spoofing, which can heavily avoid the limitations of the visible-image-based face recognition.In this paper, we summarize the spectrum properties of hyperspectral face cubes and survey the hyperspectral face recognition methods in the literature. We categorize them into major groups for better understanding. We overview the existing hyperspectral face datasets, and establish our own dataset. We also discuss efficient neural networks used for mobile face recognition and conduct experiments on mobile hyperspectral face recognition. Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube-based methods have higher recognition accuracy than visible-image-based methods. Finally, we deliver insightful discussions and prospects for future works on mobile hyperspectral face recognition.     

一种高性能的人脸识别方法

在基于人脸图像的身份认证系统中,最关键的技术就是如何提取人脸图像的高质量特征以及如何进行分类识别,该文就提出了一种快速、准确的人脸图像识别方法。该方法利用基于核函数的学习算法,进行人脸图像的特征提取和分类。首先,该方法分别利用核主分量分析以及核Fisher算法提取人脸图像的特征,然后对这些特征进行合理的组合以构成组合特征向量,再利用支持向量机进行识别。实验结果显示,所提出的高性能人脸识别方法的识别率高,即使对于轻度光照不均匀的人脸图像、人脸姿势的有限变化图像,也能获得较高的识别率;同时,该方法的训练速度和识别速度也非常快,完全满足人脸识别系统实时性要求。     

复杂背景下的人脸定位识别方法

现有的人脸识别算法多在标准库上进行,缺少对复杂背景下人脸识别问题的研究。提出一种快速的人脸定位识别方法,旨在解决复杂背景中人脸的定位和识别问题。在定位方面,提出一种新的自适应肤色分割的人脸定位算法,充分考虑类肤色背景对定位算法的影响,使该算法在户外环境下的人脸定位精度较传统方法有了一定的提高;识别方面,采用局部SVD方法提取人脸图像特征值,以PCA算法加以识别,新算法改进了传统PCA训练速度慢、内存占用大的缺陷。通过对ORL人脸库以及自制人脸库的实验分析,结果表明该方法不仅能解决复杂背景中人脸定位识别问题,并且高效、快速、有较好的实用性。     

Efficient 3D reconstruction for face recognition

Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient two-dimensional (2D)-to-three-dimensional (3D) integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and (3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.     

A Multi-View Face Recognition System

In many automatic face recognition systems,posture constraining is a key factor preventing them from application.In this paper a series of strategies will be described to achieve a system which enables face recognition under varying pose.These approaches include the multi-view face modeling,the threschold image based face feature detection,the affine transformation based face posture normalization and the template matching based face identification.Combining all of these strategies,a face recognition system with the pose invariance is designed successfully,Using a 75MHZ Pentium PC and with a database of 75 individuals,15 images for each person,and 225 test images with various postures,a very good recognition rate of 96.89% is obtained.     

DNA计算在人脸识别上的应用

提出一种将DNA计算结合奇异值分解(SVD)应用在人脸识别的方法,旨在利用其超大并行计算能力,在大规模的人脸识别中大量快速地缩小目标识别范围,从而使得后续识别能使用较为成熟的一般方法。在对ORL人脸库的实验中,有效地缩小了识别范围,使得DNA计算在人脸识别上有了新的应用。     

基于核独立成分分析的人脸识别研究

在人脸识别中提出一种基于非线性子空间的核独立成分分析(KICA)方法。在简单介绍了ICA方法的基础上,对KICA方法的基本原理和算法作了较为详细的描述。为了验证基于KICA和ICA的人脸识别方法的识别效果,进行了对比实验和分析。实验和分析结果表明,在人脸识别中,基于KICA的方法优于基于ICA的方法。     

Benchmarking lightweight face architectures on specific face recognition scenarios

Artificial Intelligence Review - This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on...     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

基于Gabor不确定度的嵌入式人脸识别系统

多尺度Gabor特征的维数和数据量过大,不适合在ARM板上直接实现完成。利用计算每个尺度Gabor特征不确定度并采用加权融合的方法,很好地解决了图像维数和数据量过大的难点。加权融合过程包括多尺度Gabor特征的提取、不确定度权值的计算和加权融合过程;同时使用了类Haar特征提取人脸、利用二维主成分分析(2DPCA)对人脸图像进行降维。基于EELiod 270嵌入式开发平台,使用ORL和Yale图像库对该方法进行了测试,并与其他人脸识别算法进行比较。结果显示,在保证识别率的同时,算法运算量大幅度下降,且实时识别效果良好。     

基于形变模型的红外人脸鲁棒识别*

针对人脸识别中的光照、表情和遮挡变化三大难题,引进热红外人脸克服光照变化,并且采用融合局部形变模型的人脸分类方法克服表情和遮挡变化。该方法将热红外测试人脸看成人脸库的线性组合,并用形变模型表示,通过 最小优化求解组合系数,根据系数的稀疏性进行人脸识别。为了进一步提高算法的鲁棒性,采用人脸分片加权的策略。在Equinox人脸库上通过大量实验表明：基于红外光的人脸识别性能明显高于可见光对光照变化的影响;融合局部形变模型的人脸识别方法可以有效地提高识别率且克服红外人脸识别中的眼镜干扰与表情变化问题。     

结构化低秩字典学习的人脸识别

目的 现实中采集到的人脸图像通常受到光照、遮挡等环境因素的影响,使得同一类的人脸图像具有不同程度的差异性,不同类的人脸图像又具有不同程度的相似性,这极大地影响了人脸识别的准确性。为了解决上述问题对人脸识别造成的影响,在低秩矩阵恢复理论的基础上提出了具有识别力的结构化低秩字典学习的人脸识别算法。方法 该算法基于训练样本的标签信息将低秩正则化以及结构化稀疏同时引入到学习的具有识别力的字典上。在字典学习过程中,首先利用样本的重建误差约束样本与字典之间的关系;其次将Fisher准则应用到稀疏编码过程中,使其编码系数具有识别能力;由于训练样本中的噪声信息会影响字典的识别力,所以在低秩矩阵恢复理论的基础上将低秩正则化应用到字典学习过程中;接着,在字典学习过程中加入了结构化稀疏使其不丢失结构信息以保证对样本进行最优分类;最后再利用误差重构法对测试样本进行分类识别。结果 本文算法在AR以及ORL人脸数据库上分别进行了实验仿真。在AR人脸数据库中,为了分析样本不同维数对实验结果造成的影响,选取了第一时期拍摄的每人6幅图像,包括1幅围巾遮挡,2幅墨镜遮挡以及3幅脸部表情变化以及光照变化（未被遮挡）的图像作为训练样本,同时选取相同组合的样本图像作为测试样本,无论哪种方法,图像的维度越高识别率越高。对比SRC （sparse representation based on classification）算法与DKSVD （discriminative K-means singular value decomposition）算法的识别率可知,DKSVD算法通过字典学习减缓了训练样本中的不确定因素对识别结果的影响;对比DLRD_SR （discriminative low-rank dictionary learning for sparse representation）算法与FDDL （Fisher discriminative dictionary learning）算法的识别率可知,当图像有遮挡等噪声信息存在时,字典低秩化可以提高至少5.8%的识别率;对比本文算法与DLRD_SR算法可知,在字典学习的过程中加入Fisher准则后识别率显著提高,同时理想稀疏值能保证对样本进行最优的分类。当样本图像的维度达到500维时人脸图像在有围巾、墨镜遮挡的情况下识别率可达到85.2%;其中墨镜和围巾的遮挡程度分别可以看成是人脸图像的20%和40%,为了验证本文算法在不同脸部表情变化、光照改变以及遮挡情况下的有效性,根据训练样本的具体图像组合情况进行实验。无论哪种样本图像组合,本文算法在有遮挡存在的样本识别中具有显著优势。在训练样本只包含脸部表情变化、光照变化以及墨镜遮挡图像的情况下,本文算法的识别率高于其他算法至少2.7%,在训练样本只包含脸部表情变化、光照变化以及围巾遮挡图像的情况下,本文算法的识别率高于其他算法至少3.6%,在训练样本包含脸部表情变化、光照变化、围巾遮挡以及墨镜遮挡图像的情况下,其识别率高于其他算法至少1.9%。在ORL人脸数据库中,人脸图像在无遮挡的情况下识别率达到95.2%,稍低于FDDL算法的识别率;在随机块遮挡程度达到20%时,相比较于SRC算法、DKSVD算法、FDDL算法以及DLRD_SR算法,本文算法的识别率最高;当随机块遮挡程度达到50%时,以上算法的识别率均不高,但本文算法的其识别率仍然最高。结论 本文算法在人脸图像受到遮挡等因素的影响时具有一定的鲁棒性,实验结果表明该算法在人脸识别方面具有可行性。     

基于光照分类的可变光照下人脸识别方法

针对人脸识别中的光照变化问题,借鉴“分而治之”的思想,提出通过光照分类来提高不同光照情况下人脸的识别率。根据人脸图像灰度随光照变化的分布特点,将图像划分为三类：无偏光类、左偏光类和右偏光类,分别在不同的光照子集中对人脸图像进行处理与识别,并在YALEB人脸库上完成实验验证。结果表明,该方法不需要进行光照归一化处理,有效减弱了光照不均匀对人脸识别的影响,在提高识别率的同时降低了运算量,识别率可从未分类前的86.7%提高到99.6%,对于可变光照下的人脸识别有一定的应用前景。     

Preserving privacy by de-identifying face images

In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers' license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by deidentifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to deidentifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This work presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize deidentified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.     

结合改进Surf特征的素描人脸识别

素描人脸识别属于异质人脸识别范畴,是刑侦领域的研究热点。根据素描人脸识别的特点,对已配准的人脸图像进行伪素描转化,并用Surf算法提取伪素描图像对的特征点。对经过提取后的伪素描特征点进行坐标邻域一致性优化,排除坐标邻域相对位置不一致的特征点,最后统计伪素描图像对的有效特征点,以实现识别的目的。利用现有的素描人脸库,进行实验验证,在选取50个特征点时的识别率达到99%,验证了算法的有效性。该算法经优化后,可用于素描人脸识别。