基于纹理分布和变形模板的面部特征提取

面部特征提取是面部感知的重要内容,同时也是特定人的3D人脸动画应用中所必须的前期工作.在一个多级人脸检测模块检测到人脸大致区域和尺寸的基础上,提出并实现了一种基于面部图像纹理分布特性和可变形模板的由粗到细的面部特征提取策略,旨在解决可变形模板对参数初值依赖性强和计算时间长的问题.该策略首先利用眼睛区域的谷特性和频率特性定位两个虹膜中心点位置,然后用积分投影确定唇部和鼻子区域的位置,在此基础上进行关键特征点的检测,从而可以得到预定义特征模板参数的良好初值,最后基于贪心算法的多阶段轮换优化算法来搜索一个极小点     

An effective method for detecting facial features and face in human-robot interaction

In this paper, an effective method of facial features detection is proposed for human-robot interaction (HRI). Considering the mobility of mobile robot, it is inevitable that any vision system for a mobile robot is bound to be faced with various imaging conditions such as pose variations, illumination changes, and cluttered backgrounds. To detecting face correctly under such difficult conditions, we focus on the local intensity pattern of the facial features. The characteristics of relatively dark and directionally different pattern can provide robust clues for detecting facial features. Based on this observation, we suggest a new directional template for detecting the major facial features, namely the two eyes and the mouth. By applying this template to a facial image, we can make a new convolved image, which we refer to as the edge-like blob map. One distinctive characteristic of this map image is that it provides the local and directional convolution values for each image pixel, which makes it easier to construct the candidate blobs of the major facial features without the information of facial boundary. Then, these candidates are filtered using the conditions associated with the spatial relationship of the two eyes and the mouth, and the face detection process is completed by applying appearance-based facial templates to the refined facial features. The overall detection results obtained with various color images and gray-level face database images demonstrate the usefulness of the proposed method in HRI applications.     

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.     

多模板ASM方法及其在人脸特征点检测中的应用

ASM(active shape model)是目前最流行的人脸对齐方法之一.为提高ASM在非均匀光照下多表情的人脸特征点检测的准确率,提出了一种融入Gabor特征、并将局部ASM和全局ASM结合的多模板ASM方法.人脸有丰富的表情,如微笑、惊讶、生气、发呆等等.就眼睛而言,可分为睁眼和闭眼;就嘴巴而言,可分为张大的嘴、微笑的嘴、O型的嘴(惊讶时)和紧闭的嘴.眼睛的这两种状态以及嘴巴的这4种状态使得形状有较大的非线性变化,不能简单地放在同一个线性模型下处理.分别对眼睛建立两个局部模板,对嘴巴建立4个局部模板,以及对整脸建立全局模板.在给定眼睛两个内眼角和嘴巴两个外嘴角的前提下,新方法首先用全局模板粗略确定眼睛所在区域,然后在此区域用眼睛的两个局部模板以及Hausdorff距离判断眼睛状态,同理可检测嘴巴状态,最后调用相应的全局模板去搜索整脸轮廓.实验表明,提出的方法其检测准确率比标准ASM有明显提高.     

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.     

Reliable detection of eye features and eyes in color facial images using ternary eye-verifier

Eye detection plays an important role in applications related to face recognition. The position of eyes can be used as a reliable reference for other facial feature detection. This paper presents a novel approach for the precise and reliable detection of eyes by introducing a ternary eye-verifier. Initially, the face region is detected by combining color information and the Haar-like feature detector. The face region is then binarized and filtered with circular filters to detect eye candidates at the peaks in the filtered response. Each eye candidate is fed into a ternary eye-verifier that includes a proposed eye feature extractor based on K-means clustering with compensation for variety in iris color. The eye template in the eye-verifier is constructed based on both the knowledge of eye geometry and the detected eye features. The template matching is made by the ternary Hamming distance. Experiments over a collection of FERET face database and house-made face database with different head poses confirm that the proposed method achieves precise and reliable detection of eyes from color facial images with variation in illumination, pose, eye gazing direction, and race.     

一种鲁棒的人脸特征定位方法

提出了一种基于AdaBoost算法和C-V方法的人脸特征定位方法。首先根据AdaBoost算法训练样本得到脸、眼、鼻、嘴4个检测器；然后结合人脸边缘图像的先验规则，使用人脸检测器提取人脸区域；接着利用眼、鼻、嘴检测器从人脸区域中检测出人脸特征所在的矩形区域；最后利用C-V方法从各个特征区域中分割出人脸特征的轮廓，进而得到人脸关键特征点的位置。在DTU IMM人脸测试集上，眼睛的检测率为100%，鼻子的检测率为95.3%，嘴巴的检测率为98.4%，提取出的特征点位置准确。实验结果表明方法是有效和鲁棒的。     

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

A key challenge of face recognition is to obtain illumination invariant face images while preserving the discriminative features. The locations and shapes of small-scale features (e.g. eyebrows, eyes, nostrils, a mouth, etc.) are usually treated as key features for face recognition. However, it has also been observed that the local texture information of facial regions contains intrinsic facial features and needs to be enhanced to improve performance. To compensate for the illumination effects that appeared while extracting both the small-scale features and the texture information, we used multiscale morphological techniques. We used a generalized dynamic morphological quotient image (GDMQI) method based on Retinex theory and multiscale morphological closing to solve the artifact problem discussed in previous works. The proposed method consisted of two main steps: (i) illumination estimation and (ii) texture enhancement. The proposed method showed improved performance when using the CMU PIE, AR and Extended Yale-B databases.     

基于人脸特征和AdaBoost算法的多姿态人脸检测

基于人脸特征和AdaBoost算法,提出一种改进的多姿态人脸检测算法。首先利用肤色特征快速排除绝大部分背景区域,然后在肤色区域中搜索眼睛和嘴巴区域,根据眼睛和嘴巴区域的几何特征所确定的人脸方向分割出大致正向的人脸候选区域,最后利用AdaBoost算法对候选区域进行分类。实验表明,算法能实现多姿态人脸的快速检测,而且对脸部表情和遮挡有较强的鲁棒性。     

Face detection and recognition of natural human emotion using Markov random fields

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research.     

Face recognition/detection by probabilistic decision-based neuralnetwork

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.     

Automatic extraction of eye and mouth fields from a face image using eigenfeatures and multilayer perceptrons

This paper presents a novel algorithm for the extraction of the eye and mouth (facial features) fields from 2-D gray-level face images. The fundamental philosophy is that eigenfeatures, derived from the eigenvalues and eigenvectors of the binary edge data set constructed from the eye and mouth fields, are very good features to locate these fields efficiently. The eigenfeatures extracted from the positive and negative training samples of the facial features are used to train a multilayer perceptron whose output indicates the degree to which a particular image window contains an eye or a mouth. It turns out that only a small number of frontal faces are sufficient to train the networks. Furthermore, they lend themselves to good generalization to non-frontal pose and even other people's faces. It has been experimentally verified that the proposed algorithm is robust against facial size and slight variations of pose.     

一种基于偏转角度的AAM人脸特征点定位方法

对有偏转角度的人脸特征点定位来说,拟合初始位置和模型的角度对人脸特征点定位效果有很大的影响。而传统的AAM（Active Appearance Models）人脸特征定位方法没有具体考虑这一问题,对有偏转角度的人脸特征点的定位准确率和速度并不理想。为解决这个问题,文中提出了一种利用两眼中心坐标和嘴中心坐标来计算人脸偏转角度,根据坐标和角度确定拟合初始位置和模板的方法。用Adaboost和YCbCr对人脸进行预检测,根据找到的特征区域计算偏转角,用反向算法结合该角度的模板进行特征点定位。实验的测试结果表明本方法对有偏转角度的人脸的特征点定位比传统方法在准确度和速度上都有了提高。     

Fovea intensity comparison code for person identification and verification

In this paper a method is proposed for person identification and verification. The method proposed in Viola and Jones (2001) is used to detect the face region in the image. The detected face region is processed to determine the locations of the eyes and mouth. The facial and mouth features are extracted relative to the locations of the eyes and mouth. A new feature called fovea intensity comparison code (FICC) is obtained from intensity values of the face/mouth region. The dimension of the FICC is reduced using principal component analysis (PCA). Euclidean distance matching is used for identification and verification. The performance of the system is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.84% for 50 subjects. The performance of the system is also evaluated for the eXtended Multi Modal Verification for Teleservices and Security (XM2VTS) database, and the system achieves a recognition rate of 100% an equal error rate (EER) of about 0.23%.     

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.     

Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

This paper presents a fuzzy relational approach to human emotion recognition from facial expressions and its control. The proposed scheme uses external stimulus to excite specific emotions in human subjects whose facial expressions are analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye opening, mouth opening, and the length of eyebrow constriction are extracted from the localized regions, fuzzified, and mapped onto an emotion space by employing Mamdani-type relational models. A scheme for the validation of the system parameters is also presented. This paper also provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust, with good accuracy.     

微表情类别与区域间关联度的分析方法研究

为了揭示微表情类别与面部各区域之间的关联度,提出一种局部区域划分和分析方法。该方法首先根据各类微表情对应动作单元所在的位置确定出人脸中最为关键的7个局部区域,然后采用面部形变鉴别模型自动检测出面部49个关键点坐标,再根据这些点确定上述局部区域的4个边界。最后,分别提取各区域的时空模式特征并进行微表情分类,通过各区域对各类微表情的分类准确率来分析两者之间的关联度。实验结果表明,局部区域划分方案科学合理,微表情“惊奇”及“厌恶”与眼睛区域、微表情“高兴”与嘴巴区域、微表情“压抑”与下巴区域有较高的关联度。     

人脸的层次化描述模型及识别研究

人脸自动识别是一个困难但有重要意义的工作。文中提出了一种基于人脸层次化描述的识别方法。该方法首先对人脸进行快速准确的特征定位及标准化,然后采用主元分析神经网络分别对定位的人脸及其特征区域进行最佳特征提取,从而得到人脸在低分辨率和较高分辨率上的两层特征描述用以识别,具有识别率高、特征数据量适中、可用于大量人像识别等特点。此方法在１３００幅人像上进行了测试,结果表明其在人脸转动、表情变化或入脸未经训练     

Fusing bio-inspired vision data for simplified high level scene interpretation: Application to face motion analysis

This paper proposes to demonstrate the advantages of using certain properties of the human visual system in order to develop a set of fusion algorithms for automatic analysis and interpretation of global and local facial motions. The proposed fusion algorithms rely on information coming from human vision models such as human retina and primary visual cortex previously developed at Gipsa-lab. Starting from a set of low level bio-inspired modules (static and moving contour detector, motion event detector and spectrum analyser) which are very efficient for video data pre-processing, it is shown how to organize them together in order to achieve reliable face motion interpretation. In particular, algorithms for global head motion analysis such as head nods, for local eye motion analysis such as blinking, for local mouth motion analysis such as speech lip motion and yawning and for open/close mouth/eye state detection are proposed and their performances are assessed. Thanks to the use of human vision model pre-processing which decorrelates visual information in a reliable manner, fusion algorithms are simplified and remain robust against traditional video acquisition problems (light changes, object detection failure, etc.).     

基于支持向量机的多姿态人脸特征定位

提出一种多姿态人脸特征定位方法,在Adaboost定位的人脸区域中划分眼、鼻和嘴的搜索区域,利用眉眼和鼻嘴整体特征,通过大规模多姿态五官样本训练的支持向量机在搜索区域中确定候选眼、鼻及嘴区域。对候选眼、鼻及嘴区域进行筛选与合并以确定最佳位置,实现多姿态人脸上五官的准确定位。实验结果表明,该方法具有较好的精确性和鲁棒性,能适应复杂背景下表情变化的多姿态人脸上的眼、鼻及嘴的定位。