marginFace: A novel face recognition method by average neighborhood margin maximization

We propose a novel appearance-based face recognition method called the marginFace approach. By using average neighborhood margin maximization (ANMM), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the global Euclidean structure of face space, ANMM aims at discriminating face images of different people based on local information. More concretely, for each face image, it pulls the neighboring images of the same person towards it as near as possible, while simultaneously pushing the neighboring images of different people away from it as far as possible. Moreover, we propose an automatic approach for determining the optimal dimensionality of the embedded subspace. The kernelized (nonlinear) and tensorized (multilinear) form of ANMM are also derived in this paper. Finally the experimental results of applying marginFace to face recognition are presented to show the effectiveness of our method.     

Comparative analysis of simple facial features extractors

In the article a certain class of feature extractors for face recognition is presented. The extraction is based on simple approaches: image scaling with pixel concatenation into a feature vector, selection of a small number of points from the face area, face image’s spectrum, and finally pixel intensities histogram. The experiments performed on several facial image databases (BioID [4], ORL face database [27], FERET [30]) show that face recognition using this class of extractors is particularly efficient and fast, and can have straightforward implementations in software and hardware systems. They can also be used in fast face recognition system involving feature-integration, as well as a tool for similar faces retrieval in 2-tier systems (as initial processing, before exact face recognition).

多姿态人脸照片的姿态估计

提出了一种基于面部图像的新的匹配系统。在这个系统中,输入的图像与各种人脸姿态的数据库图像进行比较,然后,匹配的图像给出了人脸姿态。图像数据库不仅包括各种人脸姿态,而且也包括不同的光照条件,如此,这个人脸姿态评价系统适用于不同的光照条件。对于收集各种不同面部图像,这里是通过计算机自动产生,而不是拍摄实际的照片。特征空间方法被用于寻找与输入面部图像匹配的图像。因为不同的光照图像被收集在面部图像数据库中,故提取的主特征向量主要依靠人脸姿态。由于通过选用主特征向量而减少了向量的维数,故这个匹配过程是很快的。这个姿态评价系统能够继续跟踪在不同的光照条件下不同人的人脸姿态。     

基于多分离部件稀疏编码的人脸图像分析

考虑到不同部件(眼睛,嘴等)对人脸分析的贡献差别,提出基于多部件稀疏编码的人脸图像分析方法.首先,选取对人脸(表情)分析影响较大的几个人脸部件,然后,利用多视角稀疏编码方法学习各部件的字典,并计算相应的稀疏编码,最后,将稀疏编码输入分类器(支持向量机和最小均方误差)进行判决.分别在数据库JAFFE和Yale上进行人脸(表情)识别及有遮挡的人脸(表情)识别实验.实验结果表明,基于多部件稀疏编码的人脸分析能较好地调节各部件的权重,优于各单一部件和简单的多部件融合方法的性能.     

人脸检测方法综述

人脸检测问题的提出是为了在自动人脸识别系统中的定位,近年来,由于其在安全控制、人机界面、视觉监测、基于内容的检索等领域的应用价值,开始作为一个独立的课题受到研究者的普遍重视。该文从人脸检测的模式、人脸特征的提取、人脸特征的综合、评估标准等角度,系统阐述了人脸检测系统,并将人脸检测方法分为：基于几何特征的方法,基于肤色模型的方法和基于统计理论的方法,并对各个方法的优缺点进行了比较,最后,对人脸检测今后的发展方向进行了探讨。     

Face localization for facial features extraction using a symmetrical filter and linear Hough transform

In this paper, face localization for facial feature extraction is presented. The method consists of three steps: (1) facial features enhancement using symmetrical filter, and then the morphological process is applied to examine the edge, peaks, and valley fields; (2) line construction using linear Hough transform; (3) localization of the face region based on the constructed lines and the elimination of excess areas outside the face boundary. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

机器人系统中人脸特征提取技术的研究与实现

该文描述了在智能机器人系统中人脸特征提取技术的研究与实现,提出了一种新的并且在机器人系统中实现的人脸特征提取方法,该方法首先利用基于Adaboost的人脸检测算法对采集到的原始图像进行人脸检测,从而得到人脸图像;然后让人脸图像通过一个空间掩模滤波器,去除图像中明显非人脸特征的区域,再经过二值化后得到二值化图像;将二值化图像与一个矩形模板相卷积,得到卷积值与模板索引数的二维曲线图,在二维曲线图中,最高的两个峰就分别对应了眼睛和眉毛,再根据人脸特征几何分布关系判断出眼睛,眉毛和嘴,从而得到最终的人脸特征.该方法检测率高,计算量小,实时性很强,满足了机器人系统中资源有限的约束条件.     

一种人脸标准光照图像的线性重构方法

基于相同光照下不同人脸图像与其标准光照图像之间的稳定关系,文中提出一种人脸标准光照图像重构方法。首先,为消除人脸结构影响,引入人脸三维变形,实现图像像素级对齐。其次,根据图像明暗变化,给出一种基于图像分块的光照分类方法。最后,对于形状对齐后的不同光照类别样本,训练出基于子空间的线性重构模型。该方法有效避免传统预处理方法带来的重构图像纹理丢失和子空间方法引起的图像失真。Extended Yale B数据库上实验表明,该方法对图像真实度与人脸识别率的提升,也验证文中人脸对齐和光照分类方法的有效性。     

Efficient face candidates selector for face detection

In this paper an efficient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by finding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.     

基于颜色和特征匹配的视频图像人脸检测实现技术

A face detection method using statistical skin-color model and facial feature matching is presented in this paper.According to skin-color distribution in YUV color space,we develope a statistical skin-color model through interactive sample training and learning.Using this method we convert the color image to binary image and then segment face-candidate regions in the video images.In order to improve the quality of binary image and remove unwanted noises,filtering and mathematical morphology are empolied.After these two processing,we use facial feature matching for further detection.The presence or absence of a face in each region is verified by means of mouth detector based on a template matching method.The experimental results show the proposed method has the features of high speed and high efficiency,but also robust to face variation to some extent.So it is suitable to be applied to real-time face detection and tracking in video sequences.     

COMPARING FACE IMAGES USING THE MODIFIED HAUSDORFF DISTANCE

We introduce a novel methodology applicable to face matching and fast screening of large facial databases. The proposed shape comparison method operates on edge maps and derives holistic similarity measures, yet, it does not require solving the point correspondence problem. While the use of edge images is important to introduce robustness to changes in illumination, the lack of point-to-point matching delivers speed and tolerance to local non-rigid distortions. In particular, we propose a face similarity measure derived as a variant of the Hausdorff distance by introducing the notion of a neighborhood function (N) and associated penalties (P). Experimental results on a large set of face images demonstrate that our approach produces excellent recognition results even when less than 3% of the original grey-scale face image information is stored in the face database (gallery). These results implicate that the process of face recognition may start at a much earlier stage of visual processing than it was earlier suggested. We argue, that edge-like retinal images of faces are initially screened “at a glance” without the involvement of high-level cognitive functions thus delivering high speed and reducing computational complexity.     

基于肤色及五官特征的人脸检测方法

本文提出了一种基于肤色及五官特征的人脸检测方法，通过对图片进行一系列的处理。提取出人脸的大致框架，然后通过眼睛、嘴巴的建模，最后得到图片中人脸的比较精确的位置。     

Control of Feature-point-driven Facial Animation Using a Hypothetical Face

A new approach to the generation of a feature-point-driven facial animation is presented. In the proposed approach, a hypothetical face is used to control the animation of a face model. The hypothetical face is constructed by connecting some predefined facial feature points to create a net so that each facet of the net is represented by a Coon's surface. Deformation of the face model is controlled by changing the shape of the hypothetical face, which is performed by changing the locations of feature points and their tangents. Experimental results show that this hypothetical-face-based method can generate facial expressions which are visually almost identical to those of a real face.     

Facial feature detection using distance vector fields

A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is calculated. The distance vector field of the face is extracted by assigning to every facial image pixel a vector pointing to the closest edge pixel. The x and y components of these vectors are used to detect the eyes and mouth regions. Luminance information is used for eye center localization, after removing unwanted effects, such as specular highlights, whereas the hue channel of the lip area is used for the detection of the mouth corners. The proposed method has been tested on the XM2VTS and BioID databases, with very good results.     

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%.     

证件照片的一种混合人脸识别方法

在仔细分析证件照片中人脸特点的基础上，提出了一种把人脸的几何特征矢量匹配和人脸的分块加权匹配相结合的思想。该方法针对一般人脸识别方法不能有效消除人脸表情影响的特点，首先对人脸进行快速准确的眼睛定位、图像控正以及标准化处理，然后一方面抽取能够避免人脸表情影响的几何特征向量，另一方面对标准人脸进行分块加权匹配，最后进行综合识别。对JAFFE人脸库的试验结果表明，该方法识别准确率高，能够有效地消除人脸表情在识别中的影响，结果令人满意。     

Rough membership function based illumination classifier for illumination invariant face recognition

This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow.     

A multimodal approach for 3D face modeling and recognition using 3D deformable facial mask

We present a multimodal approach for face modeling and recognition. The algorithm uses three cameras to capture stereo images, two frontal and one profile, of the face. 2D facial features are extracted from one of the frontal images and a dense disparity map is computed from the two frontal images. Using the extracted 2D features and their corresponding disparities, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project its vertices onto the frontal 2D image and adjust its profile silhouette vertices using the profile view image. We increase the resolution of the resulting 2D model at its center region to obtain a facial mask model covering distinctive features of the face. The 2D coordinates of the vertices, along with their disparities, result in a deformed 3D mask model specific to a given subject’s face. Our method integrates information from the extracted facial features from the 2D image modality with information from the 3D modality obtained from the stereo images. Application of the models in 3D face recognition, for 112 subjects, validates the algorithm with a 95% identification rate and 92% verification rate at 0.1% false acceptance rate.