A SfM-based 3D face reconstruction method robust to self-occlusion by using a shape conversion matrix

This paper presents a 3D face reconstruction method using multiple 2D face images. Structure from motion (SfM) methods, which have been widely used to reconstruct 3D faces, are vulnerable to point correspondence errors caused by self-occlusion. In order to solve this problem, we propose a shape conversion matrix (SCM) which estimates the ground-truth 2D facial feature points (FFPs) from the observed 2D FFPs corrupted by self-occlusion errors. To make the SCM, the training observed 2D FFPs and ground-truth 2D FFPs are collected by using 3D face scans. An observed shape model and a ground-truth shape model are then built to represent the observed 2D FFPs and the ground-truth 2D FFPs, respectively. Finally, the observed shape model parameter is converted to the ground truth shape model parameter via the SCM. By using the SCM, the true locations of the self-occluded FFPs are estimated exactly with simple matrix multiplications. As a result, SfM-based 3D face reconstruction methods combined with the proposed SCM become more robust against point correspondence errors caused by self-occlusion, and the computational cost is significantly reduced. In experiments, the reconstructed 3D facial shape is quantitatively compared with the 3D facial shape obtained from a 3D scanner, and the results show that SfM-based 3D face reconstruction methods with the proposed SCM show a higher accuracy and a faster processing time than SfM-based 3D face reconstruction methods without the SCM.     

基于改进ASM的三维人脸自动对齐算法

针对传统三维人脸对齐算法需要大量人工操作和数据冗余的不足,提出一种基于改进主动形状模型的三维人脸自动对齐算法。该算法对二维人脸进行快速自动特征定位,根据三维人脸柱面展开的二维纹理图进行自动特征定位并分片。采用基于平面模板的重采样算法,自动实现三维人脸顶点稠密对齐。实验结果表明,该算法在提高运算速率的同时,得到的归一化结果与原始数据仍能保持99%形状相似度。     

Face recognition based on fitting a 3D morphable model

This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.     

基于特征分块的三维人脸重建和识别

针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷,提出一种基于特征分块的三维人脸重建算法,并将此算法应用到三维人脸识别中,实现了基于特征分块的加权三维人脸识别。首先,利用基于平面模板的非均匀重采样法对原始数据进行归一化;其次,采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块;然后,利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建;最后,实现了此算法在三维人脸识别中的应用。实验表明,此重建算法具有较高的精度和重建效率,还可以达到全局最优,并且可以提高三维人脸的识别率。     

Reflectance from images: a model-based approach for human faces

In this paper, we present an image-based framework that acquires the reflectance properties of a human face. A range scan of the face is not required. Based on a morphable face model, the system estimates the 3D shape and establishes point-to-point correspondence across images taken from different viewpoints and across different individuals' faces. This provides a common parameterization of all reconstructed surfaces that can be used to compare and transfer BRDF data between different faces. Shape estimation from images compensates deformations of the face during the measurement process, such as facial expressions. In the common parameterization, regions of homogeneous materials on the face surface can be defined a priori. We apply analytical BRDF models to express the reflectance properties of each region and we estimate their parameters in a least-squares fit from the image data. For each of the surface points, the diffuse component of the BRDF is locally refined, which provides high detail. We present results for multiple analytical BRDF models, rendered at novel orientations and lighting conditions.     

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.     

基于变形与测地距离一致性约束的3D面皮点对应

针对三维面皮生理点对应关系建立这一难题,充分考虑测地距离在描述复杂几何体表面形状方面的优势,提出了基于变形与测地距离一致性约束的3D面皮生理点对应方法。首先在Frankfurt坐标变换后标定面皮特征点集,利用特征点对应关系进行TPS变形;然后根据特征点几何特征向量建立初始点对应关系集,并利用测地距离一致性约束对其进行修剪以生成对应关系核心集;最后扩展对应关系核心集,直至确定源模型上每一顶点的对应关系。实验表明,该方法提高了点对应关系准确度,可有效建立三维面皮生理点对应关系。     

Prediction of Individual Non-Linear Aging Trajectories of Faces

Represented in a Morphable Model, 3D faces follow curved trajectories in face space as they age. We present a novel algorithm that computes the individual aging trajectories for given faces, based on a non-linear function that assigns an age to each face vector. This function is learned from a database of 3D scans of teenagers and adults using support vector regression. To apply the aging prediction to images of faces, we reconstruct a 3D model from the input image, apply the aging transformation on both shape and texture, and then render the face back into the same image or into images of other individuals at the appropriate ages, for example images of older children. Among other applications, our system can help to find missing children.     

分区变形与多尺度约束结合的3D面皮点对应

针对准确建立3D面皮间点对应关系这一难题, 引入积分不变量的多尺度约束, 提出了分区变形与多尺度约束结合的3D面皮点对应方法。首先对待对应面皮和参考面皮进行法兰克福坐标矫正和特征点的标定。然后在对面皮完成测地线分区的基础上, 对各分区进行径向基函数变形, 使两个面皮的相应分区近似重合。最后在参考面皮上选取待对应点, 根据重合的面皮分区间局部相对位置的稳定性, 在待对应面皮上建立待对应点的候选集, 利用体积积分不变量的多尺度约束选定最佳对应点, 进而实现了3D面皮的点对应。实验表明, 算法有效提高了点对应的准确度。     

Robust Hausdorff distance measure for face recognition

Face is considered to be one of the biometrics in automatic person identification. The non-intrusive nature of face recognition makes it an attractive choice. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, an attempt to address these issues is made using a new Hausdorff distance-based measure. The proposed measure represent the gray values of pixels in face images as vectors giving the neighborhood intensity distribution of the pixels. The transformation is expected to be less sensitive to illumination variations besides preserving the appearance of face embedded in the original gray image. While the existing Hausdorff distance-based measures are defined between the binary edge images of faces which contains primarily structural information, the proposed measure gives the dissimilarity between the appearance of faces. An efficient method to compute the proposed measure is presented. The performance of the method on bench mark face databases shows that it is robust to considerable variations in pose, expression and illumination. Comparison with some of the existing Hausdorff distance-based methods shows that the proposed method performs better in many cases.     

Mask spoofing in face recognition and countermeasures

In this paper, initially, the impact of mask spoofing on face recognition is analyzed. For this purpose, one baseline technique is selected for both 2D and 3D face recognition. Next, novel countermeasures, which are based on the analysis of different shape, texture and reflectance characteristics of real faces and mask faces, are proposed to detect mask spoofing. In this paper, countermeasures are developed using both 2D data (texture images) and 3D data (3D scans) available in the mask database. The results show that each of the proposed countermeasures is successful in detecting mask spoofing, and the fusion of these countermeasures further improves the results compared to using a single countermeasure. Since there is no publicly available mask database, studies on mask spoofing are limited. This paper provides significant results by proposing novel countermeasures to protect face recognition systems against mask spoofing.     

Robust visual similarity retrieval in single model face databases

In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.     

3D face authentication and recognition based on bilateral symmetry analysis

We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the facial surface. The intersection between the symmetry plane and the facial surface, namely the symmetry profile, is then computed. Using both the mean curvature plot of the facial surface and the curvature plot of the symmetry profile curve, three essential points of the nose on the symmetry profile are automatically extracted. The three essential points uniquely determine a Face Intrinsic Coordinate System (FICS). Different faces are aligned based on the FICS. The symmetry profile, together with two transverse profiles, composes a compact representation, called the SFC representation, of a 3D face surface. The face authentication and recognition steps are finally performed by comparing the SFC representations of the faces. The proposed method was tested on 382 face surfaces, which come from 166 individuals and cover a wide ethnic and age variety. The equal error rate (EER) of face authentication on scans with variable facial expressions is 10.8%. For scans with normal expression, the ERR is 0.8%.     

Automatic 3D face recognition from depth and intensity Gabor features

As is well known, traditional 2D face recognition based on optical (intensity or color) images faces many challenges, such as illumination, expression, and pose variation. In fact, the human face generates not only 2D texture information but also 3D shape information. In this paper, we investigate what contributions depth and intensity information makes to face recognition when expression and pose variations are taken into account, and we propose a novel system for combining depth and intensity information to improve face recognition systems. In our system, local features described by Gabor wavelets are extracted from depth and intensity images, which are obtained from 3D data after fine alignment. Then a novel hierarchical selecting scheme embedded in linear discriminant analysis (LDA) and AdaBoost learning is proposed to select the most effective and most robust features and to construct a strong classifier. Experiments are performed on the CASIA 3D face database and the FRGC V2.0 database, two data sets with complex variations, including expressions, poses and long time lapses between two scans. Experimental results demonstrate the promising performance of the proposed method. In our system, all processes are performed automatically, thus providing a prototype of automatic face recognition combining depth and intensity information.     

Enhanced long-range personal identification based on multimodal information of human features

This work presents an enhanced long-range personal identification scheme using multimodal information of human features. Multimodal information includes multiview face detection, height measurement and face recognition. Multiview faces are estimated by collecting five face databases that correspond to left, half-left, right, half-right, and frontal faces, respectively. The sequences of parameters based on four detectors are also designed to determine the face direction. The detectors use the head-shoulder region, frontal face, profile face, and eyes detector respectively. In addition to determining when individuals enter the monitoring area, the multiview face detection module also describes the detected face direction. This result allows the identification system to select the face database of a specific direction to identify subsequent faces. Additionally, the height measurement module estimates individual height by calculating the vanishing points and lines. The module concept is based on single-view metrology. The measured information further refines the face database selected by multiview face detection and minimizes the candidates for face identification. Importantly, the proposed method integrates the multimodal information based on face direction, height and face features to refine the database and analyzes the information to determine the identity of a person. In this work, images from a monitoring area 5.6 m away from a camera are captured using an inexpensive digital web camera. The experimental results show that the proposed method can improve the accuracy rate by more than 21 % in contrast with the baselines and correspondingly demonstrates the effectiveness of the proposed idea.     

关键点匹配三维人脸识别方法*

提出了一种新颖的三维人脸识别算法,其基本思路是,把代表人脸的三维点云沿X、Y或Z轴旋转,反复多次把3D人脸关键点投影到2.5D图像上,然后提取2.5D图像的关键点并进行标记,而用这些比原来小得多的关键点代替原来的面扫描。面对未知的待测人脸首先通过执行相同的多视角特征点提取技术提取关键点,然后应用一个新的加权特征点匹配算法进行识别。通过用GavabDB三维面部识别数据集进行试验评估,这个方法对中性表情人脸可获得高达94％的识别精度,对人脸表情辨识（如微笑）的准确率也超过了88％。实验结果表明,此方法在识别精     

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.