Hybrid-boost learning for multi-pose face detection and facial expression recognition

This paper proposes a hybrid-boost learning algorithm for multi-pose face detection and facial expression recognition. To speed-up the detection process, the system searches the entire frame for the potential face regions by using skin color detection and segmentation. Then it scans the skin color segments of the image and applies the weak classifiers along with the strong classifier for face detection and expression classification. This system detects human face in different scales, various poses, different expressions, partial-occlusion, and defocus. Our major contribution is proposing the weak hybrid classifiers selection based on the Harr-like (local) features and Gabor (global) features. The multi-pose face detection algorithm can also be modified for facial expression recognition. The experimental results show that our face detection system and facial expression recognition system have better performance than the other classifiers.     

基于HSV色彩空间的自适应肤色检测

针对复杂背景彩色图像提出了一种基于HSV色彩空间的自适应肤色检测算法。该算法首先使用阈值在HSV空间对人体肤色区域进行肤色分割,然后对分割出的肤色区域使用相对重要性滤波和自适应区域归并,最后将归并后的肤色区域使用人眼定位进行验证,将多人脸检测转化为单人脸检测。实验结果表明,该算法复杂度较小,对光照变化具有很好的鲁棒性。     

运用自适应球体模型实现肤色动态分割

提出一种用于变化光照、多姿态和复杂背景条件下人脸识别的肤色区域动态分割算法。对彩色人脸输入图进行色偏校正和亮度调节预处理,利用肤色聚类特性构建一种自适应球体肤色模型,并基于该模型计算自适应肤色相似度,利用肤色相似度,采用自适应的动态阈值进行肤色区域目标的分割和提取。实验结果表明,对于变化光照、多姿态和复杂背景的彩色人脸图像,该算法有良好的分割精度和自适应性。     

基于彩色分割和特征定位的实时人脸检测算法

论文提出了一种新的彩色人脸图像实时检测算法。该算法利用皮肤颜色在YCbCr彩色空间的分布特性进行人脸皮肤区域的分割,利用人脸特征在灰度图像灰度映射极小值特性进行人脸特征定位。实验证明该算法对彩色人脸图像的检测有很好效果。     

基于肤色和Haar方差特征的人脸检测

为了在提高复杂背景下的人脸检测率的同时减少检测时间,将肤色分割和Haar方差特征相结合,在YCbCr颜色空间通过椭圆肤色模型和logistic回归分析确定每一点的肤色概率,生成肤色概率图,从而将每一点的像素值映射到[0,1],在Ostu方法的基础上采用并行的遗传算法确定肤色分割的阈值,快速分割出人脸区域;最后用少量的Haar方差特征取代原来的Haar特征,并采用SVM训练分类方法对分割出的人脸区域进行验证。实验表明,该方法不仅提高了人脸检测的正确率,而且具有较快的人脸检测速度。     

E-learning系统中情感识别的研究

传统E-learning系统存在一个最大缺点是感情缺失,为弥补这一不足,需要在其中加入面部表情识别模块.表情识别模块分为3个阶段:人脸检测,图像标准化以及情感分类.其中人脸检测作为第一个阶段,是情感识别的前提.在众多人脸检测方法中,肤色分割是一种简单快捷的方法.以肤色分割为基础,提出了一种比较简单的人脸检测算法.实验结果表明,这种方法能够有效地识别出人脸及其器官(包括眼睛和嘴巴)的位置.     

基于脸部信息和支持向量机的人脸检测

人脸检测是一个复杂而又非常有意义的模式识别问题。针对目前人脸检测算法于速度和精度不能兼优的问题,提出了一种基于脸部信息及支持向量机的人脸检测方法。算法首先利用肤色模型进行人脸粗检,然后根据人脸几何特征进行筛选,最后通过奇异值分解输入支持向量机分类。实验结果表明,该方法是十分有效的。     

Multi-cue based moving hand segmentation for gesture recognition

This paper proposes a novel moving hand segmentation approach using skin color, grayscale, depth, and motion cues for gesture recognition. The proposed approach does not depend on unreasonable restrictions, and it can solve the problem of hand-over-face occlusion. First, an online updated skin color histogram (OUSCH) model is built to robustly represent skin color; second, according to the variance information of grayscale and depth optical flow, a motion region of interest (MRoI) is adaptively extracted to locate the moving body part (MBP) and reduce the impact of noise; then, Harris-Affine corners that satisfy skin color and adaptive motion constraints are adopted as skin seed points in the MRoI; next, the skin seed points are grown to obtain a candidate hand region utilizing skin color, depth and motion criteria; finally, boundary depth gradient, skeleton extraction, and shortest path search are employed to segment the moving hand region from the candidate hand region. Experimental results demonstrate that the proposed approach can accurately segment moving hand regions under different situations, especially when the face is occluded by a hand. Furthermore, this approach achieves higher segmentation accuracy than other state-of-the-art approaches.     

基于YCbCr肤色模型和SNOW分类器的人脸检测

人脸检测是人脸识别的首要步骤,在人脸识别领域有重要的应用价值。基于YCbCr彩色空间,提出一种RGB彩色图像的人脸检测方法。该方法利用YCbCr肤色模型进行肤色分割,得到类肤色区域作为侯选人脸区域;结合split up Sparse Network OfWinnows（SNOW）分类器准确定位人脸的位置应用matlab编程技术对多组图像进行实验,结果表明,该方法适用于复杂条件下的人脸检测,并且不受人脸表情的限制,对于多人脸检测同样适用。     

Faces detection method based on skin color modeling

This study suggests a method to improve the speed of a sliding window type of face detector by way of skin color region detection. The face detection method by way of skin color region detection has been studied in various perspectives: Complicated background images because of the area whose color is similar to the skin color cause high false positive rates. In contrast, the face detection method based on appearance, which adopts a sliding window type, may involve high face detection rates but cause tremendous computational costs in the process of detection scanning as the image size increases, whereas the processing time is also extended accordingly. This study suggests a method to control the subwindow size and detection area of a sliding window by detecting and using the skin color region with the processing time reduced. By means of a face detector with haar wavelet and LBP features, 274 images were collected online in addition to Bao database images, and then an experiment was conducted with them. As a result, the face detection time in utilization of an existing sliding window decreased down to a maximum of 47%.     

Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds

A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds is presented. Visual attention, which is the cognitive process of selectively concentrating on a region of interest in the visual field, helps human to recognize objects in cluttered natural scenes. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhanced the edges and shapes within the skin colored regions. The color features used are the discretized chrominance components in HSI, YCbCr color spaces, and the similarity to skin map. The hand postures are classified using the shape and texture features, with a support vector machines classifier. A new 10 class complex background hand posture dataset namely NUS hand posture dataset-II is developed for testing the proposed algorithm (40 subjects, different ethnicities, various hand sizes, 2750 hand postures and 2000 background images). The algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds. The algorithm provided a recognition rate of 94.36 %. A comparison of the proposed algorithm with other existing methods evidences its better performance.     

一种结合肤色及类人脸特征的人脸检测

人脸特征提取是人脸检测的关键环节,有效的人脸特征将使得人脸检测更精确。Haar-Like特征作为一种矩形特征,虽然简单、计算迅速,但只能描述特定方向的图形结构。提出的类人脸特征是一种反映人脸灰度分布模型的矩形特征,更加有效地描述了人脸的特征。所提出的人脸检测算法,应用BP神经网络算法训练肤色区域,进行肤色分割。应用类人脸特征的AdaBoost算法进行人脸检测。实验结果表明,该算法可以提高人脸检测的检测率。     

基于CbCgCr椭球体似然平滑度的肤色分割

对于有色偏、高光和阴影的复杂背景彩色人脸图像,已有模型不能获得较理想的分割结果。为此,提出一种基于CbCgCr椭球体肤色似然平滑度的肤色分割算法。预处理人脸输入图,以消除色偏、高光和阴影,建立CbCgCr椭球体肤色模型,计算肤色似然平滑度,利用肤色似然平滑度粗分割肤色区域,并对其进行去噪处理,以获得肤色细分割图和细提取图。实验结果表明,对光照不均和背景复杂的人脸彩色图像,该算法的肤色分割准确性、鲁棒性和实时性较优。     

肤色模型的面部肤色区域提取

面部肤色区域提取是数字图像处理和模式识别领域的研究热点。研究了采用OpenCV分类器实现人脸检测的方法,并基于肤色区域的聚类特性,构造出一个肤色模型查找表,进而实现了面部肤色区域的提取。实验证明,这种方法具有良好的适应性和实时性。     

基于标准肤色的人脸图像纹理合成与三维重建应用

本文提出一种基于单幅人脸图像并结合标准肤色的人脸图像纹理合成和三维重建算法.首先,利用ASM算法提取人脸特征点,并通过基于局部线性嵌入算法的编辑传播实现颜色转换,使图像人脸色调与三维人脸模型标准肤色一致.接着,将人脸图像五官区域与标准肤色图进行泊松融合,并考虑眉毛遮挡情况,利用人脸对称性或眉毛模板还原眉毛.尤其对于半遮挡眉毛,采用Li模型和角点检测相结合的方法重建眉毛轮廓,得到最终人脸纹理图.最后通过纹理映射将人脸纹理图映射到三维人脸模型上,得到较好的个性化三维人脸重建效果.实验表明,本文算法能够适用于不同复杂背景和光照条件下拍摄的人脸图像,具有较快的处理速度,能够应用于人脸实时重建产品中.     

融合双肤色模型及AdaBoost算法的人脸检测

为提高人脸检测的精度,提出一种融合双肤色模型与Adaboost算法的人脸检测方法。首先采用YCbCr颜色空间的固定阈值模型初次分割图像,利用分割结果修正高斯肤色模型的参数并对图像进行肤色二次分割,对两次分割的结果进行逻辑运算,粗定位人脸区域。结合Adaboost算法,实现对候选人脸区域的精确定位。实验结果表明,该方法提高了人脸检测率,降低了误检率,具有较好的鲁棒性。     

Semi self-training beard/moustache detection and segmentation simultaneously

This paper presents a robust, fully automatic and semi self-training system to detect and segment facial beard/moustache simultaneously in challenging facial images. Based on the observation that some certain facial areas, e.g. cheeks, do not typically contain any facial hair whereas the others, e.g. brows, often contain facial hair, a self-trained model is first built using a testing image itself. To overcome the limitation of that facial hairs in brows regions and beard/moustache regions are different in length, density, color, etc., a pre-trained model is also constructed using training data. The pre-trained model is only pursued when the self-trained model produces low confident classification results. In the proposed system, we employ the superpixel together a combination of two classifiers, i.e. Random Ferns (rFerns) and Support Vector Machines (SVM) to obtain good classification performance as well as improve time efficiency. A feature vector, consisting of Histogram of Gabor (HoG) and Histogram of Oriented Gradient of Gabor (HOGG) at different directions and frequencies, is generated from both the bounding box of the superpixel and the super pixel foreground. The segmentation result is then refined by our proposed aggregately searching strategy in order to deal with inaccurate landmarking points. Experimental results have demonstrated the robustness and effectiveness of the proposed system. It is evaluated in images drawn from three entire databases i.e. the Multiple Biometric Grand Challenge (MBGC) still face database, the NIST color Facial Recognition Technology FERET database and a large subset from Pinellas County database.     

基于肤色分割、区域分析和模板分布的人脸检测研究

提出了一种基于肤色分割、区域分析和模板分布的彩色图像人脸检测算法。首先对输入的彩色图像利用混合高斯模型和亮度模型进行分割,然后根据人脸五官的结构特征对得到的区域进一步分析处理,获得所有可能的候选人脸。接着构造了一种基于双眼和人脸模板的概率模型并利用其对候选人脸进行最终检测。实验结果表明,文章提出的算法具有较高的检测正确率和自适应能力;同时具有快速的检测速度。     

Face detection in color images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors)     

基于多特征提取的人脸检测

针对单一特征在人脸检测方面的不足,提出了一种基于多特征提取的人脸检测算法。利用肤色信息分割出候选人脸区域,并对其进行小波分析,降低维数。进行离散余弦变换,取出部分系数作为频率域特征。对变换后的重构图像利用奇异值分解和局部二值模式提取代数特征和纹理特征,将这三方面特征融合成新的特征向量。这样既降低了维数,又综合了三方面的特征优势,保证了利用支持向量机分类,定位人脸的效果。实验结果表明,该方法具有较高的检测率,且鲁棒性较好。