Face recognition using discriminative locality preserving vectors

We proposed an effective face recognition method based on the discriminative locality preserving vectors method (DLPV). Using the analysis of eigenspectrum modeling of locality preserving projections, we selected the reliable face variation subspace of LPP to construct the locality preserving vectors to characterize the data set. The discriminative locality preserving vectors (DLPV) method is based on the discriminant analysis on the locality preserving vectors. Furthermore, the theoretical analysis showed that the DLPV is viewed as a generalized discriminative common vector, null space linear discriminant analysis and null space discriminant locality preserving projections, which gave the intuitive motivation of our method. Extensive experimental results obtained on four well-known face databases (ORL, Yale, Extended Yale B and CMU PIE) demonstrated the effectiveness of the proposed DLPV method.     

3D face recognition with sparse spherical representations

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a fully automated registration process. They are then represented as signals on the 2-sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where linear discriminant analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images.     

鲁棒可预测判别字典学习人脸识别方法

提出一种可预测判别K-SVD网络模型(DKSVDN)并用于人脸识别问题。该模型构造了一种新颖的字典结构,包含类别标签字典和描述字典,以兼顾判别和重构性能。相应的稀疏编码向量由标签编码向量和描述编码向量组成。针对样本稀疏编码时间效率低的问题,利用预测神经网络与判别字典学习模型协同训练的方法来加速预测稀疏编码。此外,针对DKSVDN还特别引入一种拟梦境的训练方法用于提升模型在训练集多样性不足时的鲁棒性。通过在主流人脸数据集上的对比实验证明了该模型的优良性能。     

Extracting discriminative color features for face recognition

This paper presents a discriminative color features (DCF) method, which applies a simple yet effective color model, a novel similarity measure, and effective color feature extraction methods, for improving face recognition performance. First, the new color model is constructed according to the principle of Ockham’s razor from a number of available models that take advantage of the subtraction of the primary colors for boosting pattern recognition performance. Second, the novel similarity measure integrates both the angular and the distance information for improving upon the broadly applied similarity measures. Finally, the discriminative color features are extracted from a compact color image representation by means of discriminant analysis with enhanced generalization capabilities. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show the feasibility of the proposed method.     

Bilinear discriminative dictionary learning for face recognition

This work presents a novel dictionary learning method based on the _l2$l_2$-norm regularization to learn a dictionary more suitable for face recognition. By optimizing the reconstruction error for each class using the dictionary atoms associated with that class, we learn a structured dictionary which is able to make the reconstruction error for each class more discriminative for classification. Moreover, to make the coding coefficients of samples coded over the learned dictionary discriminative, a discriminative term bilinear to the training samples and the coding coefficients is incorporated in our dictionary learning model. The bilinear discriminative term essentially resolves a linear regression problem for patterns concatenated by the training samples and the coding coefficients in the Reproducing Kernel Hilbert Space (RKHS). Consequently, a novel classifier based on the bilinear discriminative model is also proposed. Experimental results on the AR, CMU PIE, CAS-PEAL-R1, and the Sheffield (previously UMIST) face databases show that the proposed method is effective to expression, lighting, and pose variations in face recognition as well as gender classification, compared with the recently proposed face recognition methods and dictionary learning methods.     

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.     

Real-time triple product relighting using spherical local-frame parameterization

This paper addresses the problem of real-time rendering for objects with complex materials under varying all-frequency illumination and changing view. Our approach extends the triple product algorithm by using local-frame parameterization, spherical wavelets, per-pixel shading and visibility textures. Storing BRDFs with local-frame parameterization allows us to handle complex BRDFs and incorporate bump mapping more easily. In addition, it greatly reduces the data size compared to storing BRDFs with respect to the global frame. The use of spherical wavelets avoids uneven sampling and energy normalization of cubical parameterization. Finally, we use per-pixel shading and visibility textures to remove the need for fine tessellations of meshes and shift most computation from vertex shaders to more powerful pixel shaders. The resulting system can render scenes with realistic shadow effects, complex BRDFs, bump mapping and spatially-varying BRDFs under varying complex illumination and changing view at real-time frame rates on modern graphics hardware.     

Mixture of spherical distributions for single-view relighting

We present a method for simultaneously estimating the illumination of a scene and the reflectance property of an object from single view images - a single image or a small number of images taken from the same viewpoint. We assume that the illumination consists of multiple point light sources and the shape of the object is known. First, we represent the illumination on the surface of a unit sphere as a finite mixture of von Mises-Fisher distributions based on a novel spherical specular reflection model that well approximates the Torrance-Sparrow reflection model. Next, we estimate the parameters of this mixture model including the number of its component distributions and the standard deviation of them, which correspond to the number of light sources and the surface roughness, respectively. Finally, using these results as the initial estimates, we iteratively refine the estimates based on the original Torrance-Sparrow reflection model. The final estimates can be used to relight single-view images such as altering the intensities and directions of the individual light sources. The proposed method provides a unified framework based on directional statistics for simultaneously estimating the intensities and directions of an unknown number of light sources as well as the specular reflection parameter of the object in the scene.     

Graph regularized discriminative non-negative matrix factorization for face recognition

Non-negative matrix factorization (NMF) has been widely employed in computer vision and pattern recognition fields since the learned bases can be interpreted as a natural parts-based representation of the input space, which is consistent with the psychological intuition of combining parts to form a whole. In this paper, we propose a novel constrained nonnegative matrix factorization algorithm, called the graph regularized discriminative non-negative matrix factorization (GDNMF), to incorporate into the NMF model both intrinsic geometrical structure and discriminative information which have been essentially ignored in prior works. Specifically, both the graph Laplacian and supervised label information are jointly utilized to learn the projection matrix in the new model. Further we provide the corresponding multiplicative update solutions for the optimization framework, together with the convergence proof. A series of experiments are conducted over several benchmark face datasets to demonstrate the efficacy of our proposed GDNMF.     

Face recognition under varying illumination based on a 2D face shape model

This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed using BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the category of its light source. In our scheme, we divide the light source for a human face into 65 categories. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that will visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information about a human face, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis for face recognition, the recognition rate can be improved by 53.3% to 62.6% when our proposed algorithm for lighting compensation is used.     

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.     

Face recognition in 2D and 2.5D using ridgelets and photometric stereo

A new technique for face recognition – Ridgefaces – is presented. The method combines the well-known Fisherface method with the ridgelet transform and high-speed Photometric Stereo (PS). The paper first derives ridgelet projections for 2D/2.5D face images before the Fisherface approach is used to reduce the dimensionality and increase the spread of the resulting feature vectors. The ridgelet transform is attractive because it is efficient at extracting highly discriminating low-frequency directional features. Best recognition is obtained when Ridgefaces is performed on surface normals acquired from PS, although good results are also found using standard 2D images and PS-derived albedo maps.     

置信度判别嵌入式隐马尔可夫模型人脸识别

为了提高人脸识别率,提出了一种优化置信度的判别嵌入式隐马尔可夫（EHMM）人脸识别方法。提出的方法基于假设检验,通过最小化检验错误率得到优化置信度判别式训练准则。在优化置信度判别式训练准则的前提下,通过参数估计求解判别式转换矩阵,提取出具有判别性、低维度的图像特征,确保观察样本能正确地分配到其对应的模型状态,以提高所训练出的EHMM模型的正确识别率。理论分析证明了优化置信度判别式训练准则的有效性,详细的实验及与现有方法的比较结果表明,提出的识别方法具有更好的识别性能。     

Shadow compensation in 2D images for face recognition

Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for face recognition. Since most human faces are similar in shape, we can find the shadow characteristics, which the illumination variation makes on the faces depending on the direction of light. By using these characteristics, we can compensate for the illumination variation on face images. The proposed method is simple and requires much less computational effort than the other methods based on 3D models, and at the same time, provides a comparable recognition rate.     

Object recognition using discriminative parts

The existing object recognition methods can be classified into two categories: interest-point-based and discriminative-part-based. The interest-point-based methods do not perform well if the interest points cannot be selected very carefully. The performance of the discriminative-part-base methods is not stable if viewpoints change, because they select discriminative parts from the interest points. In addition, the discriminative-part-based methods often do not provide an incremental learning ability. To address these problems, we propose a novel method that consists of three phases. First, we use some sliding windows that are different in scale to retrieve a number of local parts from each model object and extract a feature vector for each local part retrieved. Next, we construct prototypes for the model objects by using the feature vectors obtained in the first phase. Each prototype represents a discriminative part of a model object. Then, we establish the correspondence between the local parts of a test object and those of the model objects. Finally, we compute the similarity between the test object and each model object, based on the correspondence established. The test object is recognized as the model object that has the highest similarity with the test object. The experimental results show that our proposed method outperforms or is comparable with the compared methods in terms of recognition rates on the COIL-100 dataset, Oxford buildings dataset and ETH-80 dataset, and recognizes all query images of the ZuBuD dataset. It is robust enough for distortion, occlusion, rotation, viewpoint and illumination change. In addition, we accelerate the recognition process using the C4.5 decision tree technique, and the proposed method has the ability to build prototypes incrementally.     

2.5D face recognition using Patch Geodesic Moments

In this paper, we propose a novel Patch Geodesic Distance (PGD) to transform the texture map of an object through its shape data for robust 2.5D object recognition. Local geodesic paths within patches and global geodesic paths for patches are combined in a coarse to fine hierarchical computation of PGD for each surface point to tackle the missing data problem in 2.5D images. Shape adjusted texture patches are encoded into local patterns for similarity measurement between two 2.5D images with different viewing angles and/or shape deformations. An extensive experimental investigation is conducted on 2.5 face images using the publicly available BU-3DFE and Bosphorus databases covering face recognition under expression and pose changes. The performance of the proposed method is compared with that of three benchmark approaches. The experimental results demonstrate that the proposed method provides a very encouraging new solution for 2.5D object recognition.     

人脸识别:从二维到三维

人脸识别是生物特征识别技术的一个重要方向。虽然目前大部分研究都还只是针对二维人脸图像,但是3D人脸模型包含更丰富的人脸信息,有助于机器对人脸的识别。从二维到三维,人脸识别研究进入了一个新的阶段。从3D人脸数据的获取方式入手,介绍最近提出的一系列3D人脸识别算法,并进行归类。最后提出"有针对性地获取3D人脸模型数据是进行有效识别的基础"这一结论。