3-D face recognition method based on optimum 3-D image measurement technology

Most face recognition systems employ 2-D color or gray-scale images. However, face recognition based on 2-D images is adversely affected by 3-D movement, variable lighting, and the use of cosmetics. 3-D image measurement technology has the potential to overcome these limitations of face recognition based on 2-D images since it can perform geometric analysis. We propose a method that is capable of recognizing a person from a 3-D facial image obtained using a 3-D shape measurement system by employing a technique that optimizes the intensity-modulation pattern projection. This face recognition method is based on the iterative closest point algorithm. It is robust to changes in reflectivity and color. Since the 3-D facial information can be registered, this method can estimate rotations and translations to compensate for different positions or directions. In order to prove the validity of the proposed technique, a verification experiment was conducted which used 105 sample 3-D images obtained from 15 subjects. It achieved a detection rate of 96% when heads were turned at an angle of 20° or less relative to the camera.     

DCT系数重组在特征脸中的应用研究

针对现在图像压缩主要采用DCT变换方法,提出了一种对DCT系数进行重组的方法,获取三级多分辨率子带。通过对各单独子带实施特征脸方法可得到不同的识别效果。提出在子带[S0]和子带[S1],[S2]和[S3]上进行特征脸方法识别,如两者结果不一致,则在满足一定门限条件下输入多张人脸,分析了所提出方法与原方法所需识别时间的关系。在ORL库和YALE库上的实验表明,提出方法的识别率要远高于直接采用特征脸方法。     

Identifying Noncooperative Subjects at a Distance Using Face Images and Inferred Three-Dimensional Face Models

We present an approach to identify noncooperative individuals at a distance from a sequence of images, using 3-D face models. Most biometric features (such as fingerprints, hand shape, iris, or retinal scans) require cooperative subjects in close proximity to the biometric system. We process images acquired with an ultrahigh-resolution video camera, infer the location of the subjects' head, use this information to crop the region of interest, build a 3-D face model, and use this 3-D model to perform biometric identification. To build the 3-D model, we use an image sequence, as natural head and body motion provides enough viewpoint variation to perform stereomotion for 3-D face reconstruction. We have conducted experiments on a 2-D and 3-D databases collected in our laboratory. First, we found that metric 3-D face models can be used for recognition by using simple scaling method even though there is no exact scale in the 3-D reconstruction. Second, experiments using a commercial 3-D matching engine suggest the feasibility of the proposed approach for recognition against 3-D galleries at a distance (3, 6, and 9 m). Moreover, we show initial 3-D face modeling results on various factors including head motion, outdoor lighting conditions, and glasses. The evaluation results suggest that video data alone, at a distance of 3 to 9 meters, can provide a 3-D face shape that supports successful face recognition. The performance of 3-D–3-D recognition with the currently generated models does not quite match that of 2-D–2-D. We attribute this to the quality of the inferred models, and this suggests a clear path for future research.      

可变光照条件下的人脸图像识别

对于人脸图像识别中光照变化的影响,传统的解决方法是对待识别图像进行光照补偿,先使它成为标准光照条件下的图像,然后和模板图像匹配来进行识别。为了提高在光照条件大范围变化时,人脸图像的识别率,提出了一种新的可变光照条件下的人脸图像识别方法。该方法首先利用在9个基本光照方向下分别获得的9幅图像来构成人脸光照特征空间,再通过这个光照特征空间,将图像库中的人脸图像变换成与待识别图像具有相同光照条件的图像,并将其作为模板图像;然后利用特征脸方法进行识别。实验结果表明,这种方法不仅能够有效地解决人脸识别中由于光照变化影响所造成的识别率下降的问题,而且对于光照条件大范围变化的情况,也可以得到比较高的正确识别率。     

Face recognition by applying wavelet subband representation and kernel associative memory

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.     

Classification of face images using local iterated function systems

There has been an increasing interest in face recognition in recent years. Many recognition methods have been developed so far, some very encouraging. A key remaining issue is the existence of variations in the input face image. Today, methods exist that can handle specific image variations. But we are yet to see methods that can be used more effectively in unconstrained situations. This paper presents a method that can handle partial translation, rotation, or scale variations in the input face image. The principal is to automatically identify objects within images using their partial self-similarities. The paper presents two recognition methods which can be used to recognise objects within images. A face recognition system is then presented that is insensitive to limited translation, rotation, or scale variations in the input face image. The performance of the system is evaluated through four experiments. The results show that the system achieves higher recognition rates than those of a number of existing approaches. The author would like to thank the Australian Research Council (ARC) which supports this research with a Discovery Grant.     

A Multimodal Approach for Face Modeling and Recognition

In this paper, we present a fully automated multi- modal (3-D and 2-D) face recognition system. For the 3-D modality, we model the facial image as a 3-D binary ridge image that contains the ridge lines on the face. We use the principal curvature $kappa_{rm max}$ to extract the locations of the ridge lines around the important facial regions on the range image (i.e., the eyes, the nose, and the mouth.) For matching, we utilize a fast variant of the iterative closest point to match the ridge image of a given probe image to the archived ridge images in the database. The main advantage of this approach is reducing the computational complexity by two orders of magnitude by relying on the ridge lines. For the 2-D modality, we model the face by an attributed relational graph (ARG), where each node of the graph corresponds to a facial feature point. At each facial feature point, a set of attributes is extracted by applying Gabor wavelets to the 2-D image and assigned to the node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of geometrical features that defines the mutual relations between the edges is extracted from the Delaunay triangles and stored in the ARG model. The similarity measure between the ARG models that represent the probe and gallery images is used for 2-D face recognition. Finally, we fuse the matching results of the 3-D and the 2-D modalities at the score level to improve the overall performance of the system. Different techniques for fusion, such as the Dempster–Shafer theory of evidence and weighted sum of scores are employed and tested using the facial images in the third experiment dataset of the Face Recognition Grand Challenge version 2.0.      

基于多分类器组合的多角度彩色人脸图像检测

提出了基于神经网络和隐马尔可夫模型组合的彩色人脸图像检测方法 .根据归一化后的彩色图像的色度彩色分量直方图将图像粗分割成若干幅二值图像 ;在亮度图像上 ,以上述二值图像为掩模进行多分辨率的旋转不变性人脸检测 .在人脸检测时 ,本文分两步 :第一步先用神经网络来确定人脸的旋转角度 ,然后对旋正后的图像运用识别人脸奇异值特征的隐马尔可夫模型进行验证 .实验结果表明 ,本文算法是有效的     

基于球面谐波基图像的任意光照下的人脸识别

提出了一种基于球面谐波基图像的光照补偿算法,用以在任意光照条件下进行人脸识别.算法分两步进行：光照估计和光照补偿.基于人脸形状大致相同和每个人脸的反射率基本相等的假设,首先估计了输入人脸图像光照的9个低频谐波系数.根据光照估计的结果,提出了两种光照补偿方法：纹理图像和差图像.纹理图像为输入图像与其光照辐照图之商,与输入图像的光照条件无关.差图像为输入图像与平均人脸在相同光照下的图像之差,通过减去平均人脸在相同光照下的图像,减弱了光照的影响.在CMU-PIE人脸库和Yale B人脸库上的实验表明,通过光照补偿,不同光照下人脸图像识别率有了很大提高.     

PCA-based image recombination for multimodal 2D + 3D face recognition

Most of the existing approaches of multimodal 2D + 3D face recognition exploit the 2D and 3D information at the feature or score level. They do not fully benefit from the dependency between modalities. Exploiting this dependency at the early stage is more effective than the later stage. Early fusion data contains richer information about the input biometric than the compressed features or matching scores. We propose an image recombination for face recognition that explores the dependency between modalities at the image level. Facial cues from the 2D and 3D images are recombined into a more independent and discriminating data by finding transformation axes that account for the maximal amount of variances in the images. We also introduce a complete framework of multimodal 2D + 3D face recognition that utilizes the 2D and 3D facial information at the enrollment, image and score levels. Experimental results based on NTU-CSP and Bosphorus 3D face databases show that our face recognition system using image recombination outperforms other face recognition systems based on the pixel- or score-level fusion.     

Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models

In this paper, we present a novel segmentation-free Arabic handwriting recognition system based on hidden Markov model (HMM). Two main contributions are introduced: a new technique for dividing the image into nonuniform horizontal segments to extract the features and a new technique for solving the problems of the skewing of characters by fusing multiple HMMs. Moreover, two enhancements are introduced: the pre-processing method and feature extraction using concavity space. The proposed system first pre-processes the input image by setting the thickness of the input word to three pixels and fixing the spacing between the different parts of the word. The input image is divided into constant number of nonuniform horizontal segments depending on the distribution of the foreground pixels. A set of robust features representing the gradient of the foreground pixels is extracted using sliding windows. The input image is decomposed into several images representing the vertical, horizontal, left diagonal and right diagonal edges in the image. A set of robust features representing the densities of the foreground pixels in the various edge images is extracted using sliding windows. The proposed system builds character HMM models and learns word HMM models using embedded training. Besides the vertical sliding window, two slanted sliding windows are used to extract the features. Three different HMMs are used: one for the vertical sliding window and two for the slanted windows. A fusion scheme is used to combine the three HMMs. The proposed system is very promising and outperforms all the other Arabic handwriting recognition systems reported in the literature.     

2D representation of facial surfaces for multi-pose 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the intrinsic difficulties faced by conventional face recognition using 2D images. Instead of extending 2D recognition algorithms for 3D purpose, this letter proposes a novel strategy for 3D face recognition from the perspective of representing each 3D facial surface with a 2D attribute image and taking the advantage of the advances in 2D face recognition. In our approach, each 3D facial surface is mapped homeomorphically onto a 2D lattice, where the value at each site is an attribute that represents the local 3D geometrical or textural properties on the surface, therefore invariant to pose changes. This lattice is then interpolated to generate a 2D attribute image. 3D face recognition can be achieved by applying the traditional 2D face recognition techniques to obtained attribute images. In this study, we chose the pose invariant local mean curvature calculated at each vertex on the 3D facial surface to construct the 2D attribute image and adopted the eigenface algorithm for attribute image recognition. We compared our approach to state-of-the-art 3D face recognition algorithms in the FRGC (Version 2.0), GavabDB and NPU3D database. Our results show that the proposed approach has improved the robustness to head pose variation and can produce more accurate 3D multi-pose face recognition.     

基于FLDA、CPCA与HMM的人脸识别

为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。     

Illumination invariant extraction for face recognition using neighboring wavelet coefficients

The features of a face can change drastically as the illumination changes. In contrast to pose position and expression, illumination changes present a much greater challenge to face recognition. In this paper, we propose a novel wavelet based approach that considers the correlation of neighboring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. Our method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based NeighShrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. More importantly, by having different processes, a simple processing algorithm with low time complexity can be applied to the testing image. This leads to an easy application to real face recognition systems. Experimental results on Yale face database B and CMU PIE Face Database show that excellent recognition rates can be achieved by the proposed method.     

A novel framework for automatic 3D face recognition using quality assessment

The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance. In this paper, we present a novel 3D face recognition system which performs quality assessment on input images prior to recognition. More specifically, a reject option is provided to allow the system operator to eliminate the incoming images of poor quality, e.g. failure acquisition of 3D image, exaggerated facial expressions, etc.. Furthermore, an automated approach for preprocessing is presented to reduce the number of failure cases in that stage. The experimental results show that the 3D face recognition performance is significantly improved by taking the quality of 3D facial images into account. The proposed system achieves the verification rate of 97.09% at the False Acceptance Rate (FAR) of 0.1% on the FRGC v2.0 data set.     

基于隐马尔科夫模型的人脸识别

对于人脸识别系统来说,人脸图像的特征提取和匹配是决定人脸识别系统性能的关键所在。文中提出基于隐马尔科夫模型的人脸识别方法。首先,根据人脸的特点建立马尔科夫模型,然后对图像进行预处理,再利用采样窗对人脸图像进行采样并进行离散余弦变换,提取变换后的系数作为观察向量。最后对人脸图像进行HMM训练,训练结束后即建立了一个人的HMM。基于DCT系数的二维隐马尔科夫模型由于充分利用了人脸图像的二维统计特性,具有较高的识别率。实验结果证明此方法在准确性方面具有良好的性能。     

基于BP神经网络的人脸识别方法

人脸自动识别是计算机模式识别领域的一个活跃课题,有着十分广泛的应用前景。文中提出了基于ＢＰ神经网络的人脸识别方法,论述了人脸图像矢量的特征压缩问题、网络隐含层神经元数选取问题、网络输入矢量的标准化处理问题以及网络连接权值选取问题。对于１８人、每人１２幅图像组成的脸图像数据库做识别实验,实验结果表明文中所设计的神经网络分类器比常用的最近邻分类器有效地降低了识别错误率。     

Learning the Uncorrelated, Independent, and Discriminating Color Spaces for Face Recognition

This paper presents learning the uncorrelated color space (UCS), the independent color space (ICS), and the discriminating color space (DCS) for face recognition. The new color spaces are derived from the RGB color space that defines the tristimuli R, G, and B component images. While the UCS decorrelates its three component images using principal component analysis (PCA), the ICS derives three independent component images by means of blind source separation, such as independent component analysis (ICA). The DCS, which applies discriminant analysis, defines three new component images that are effective for face recognition. Effective color image representation is formed in these color spaces by concatenating their component images, and efficient color image classification is achieved using the effective color image representation and an enhanced Fisher model (EFM). Experiments on the face recognition grand challenge (FRGC) and the biometric experimentation environment (BEE) show that for the most challenging FRGC version 2 Experiment 4, which contains 12 776 training images, 16 028 controlled target images, and 8014 uncontrolled query images, the ICS, DCS, and UCS achieve the face verification rate (ROC III) of 73.69%, 71.42%, and 69.92%, respectively, at the false accept rate of 0.1%, compared to the RGB color space, the 2-D Karhunen-Loeve (KL) color space, and the FRGC baseline algorithm with the face verification rate of 67.13%, 59.16%, and 11.86%, respectively, with the same false accept rate.     

卷积神经网络的人脸隐私保护识别

目的 近年来，随着人脸识别认证技术的发展及逐渐普及，大量人脸照片存放在第三方服务器上的现象十分普遍，如何对人脸进行隐私保护这个问题变得十分突出。方法 首先对人脸图像进行预处理，然后采用Arnold变换对人脸关键部位进行分块随机置乱，并将置乱结果图输入到深度卷积神经网络中。为了解决人脸照片在分块置乱时由于本身拍照角度的原因导致的分块不均等因素，在预处理时根据人眼进行特性点定位，再据此进行对齐处理，使得预处理后的照片人眼处于同一水平线。针对人脸隐私保护及加扰置乱后图像的识别，本文提出了基于分块随机加扰的深度卷积神经网络模型。不包含附加层，该模型网络结构由4个卷积层、3个池化层、1个全连接层和1个softmax回归层组成。服务器端通过深度神经网络模型直接对置乱后人脸图像进行验证识别。结果 该算法使服务器端全程不存储原始人脸模板，实现了对原始人脸图像的有效加扰保护。实验采用该T深度卷积神经网络对处理过后的ORL人脸库进行识别，最终识别准确率达到97.62%。同时通过多组对比实验，验证了本文方法的有效性。结论 与其他文献中手工提取特征并利用决策树和随机森林进行训练识别的方法相比，本文方法减少了人工提取特征的工作量，且具有高识别率。