Description: 对由光源颜色变化引起的图像色彩偏差,进行了校正,并在YCbCr颜色空间建立了Cb-Cr色度查找表和亮度信息联合的肤色模型,应用预处理技术,去除部分非人脸区域,减少人脸检测的搜索空间,并采用模板匹配方法在人脸候选区域检测人脸.实验表明,该方法能够有效的从复杂环境的彩色图像中检测出左右旋转不超过45°的人脸,且不受人脸表情、尺度和数目的影响,且错误率较低.-Color by the light source caused by the change of image color deviation, a correction, and YCbCr color space established a Cb-Cr chrominance and luminance information look-up table of the color model of the joint application of pre-treatment technology, to remove some non-human face region, Face detection to reduce the search space, using a template matching method in the face candidate region detection of human faces. experiments show that the method can be effective in complex environments from color images detected no more than about 45 ° rotation of the human face, and from Facial Expression, scale and number of impact, and lower error rate. Platform: |
Size: 191488 |
Author:lll |
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Description: ：通过人眼图像来检测驾驶员疲劳驾驶是目前的主流方向，面部及眼睛定位是其中关键的环节。针对驾驶员疲
劳检测系统，结合图像处理和模式识别技术，提出一种基于人脸面部特征的人眼定位方法。经实验验证：该方法实时性
好，可用于不同背景、光照、旋转和偏转角度，以及睁闭眼、戴眼镜等多种复杂条件下的眼睛定位。-: Adoption of the human eye images to detect driver fatigue is the main driving direction, face and eyes positioning is the key link. For driver fatigue detection system, combined with the image processing and pattern recognition technology, presents a human face on the Department of the characteristics of the human eye positioning methods. After experimental verification: the method in real time and can be used for different backgrounds, light, rotation and偏转角度, as well as the open eyes closed, wearing glasses and many other complex under the conditions of the eye positioning. Platform: |
Size: 94208 |
Author:张海水 |
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Description: face detection
Face detection can be regarded as a more general case of face localization In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.
Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both.-face detection
Face detection can be regarded as a more general case of face localization In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.
Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both. Platform: |
Size: 13312 |
Author:gianni |
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Description: Head Rotation Estimation Algorithm for Hand-Free Computer Interaction by Rafał Kozik-In the article robust method of hands-free interaction with computer is proposed and tested. There are showed the results of algorithms based on optical flow and rapid face detection. Platform: |
Size: 368640 |
Author:Ng Jack |
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Description: This a software utility. It provides auto attendance management system using face detection technique. Now a day’s many utilities provide face detection system but not intelligent. Our project is more intelligent, cost less and very easy to configure anywhere.
Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex. -This is a software utility. It provides auto attendance management system using face detection technique. Now a day’s many utilities provide face detection system but not intelligent. Our project is more intelligent, cost less and very easy to configure anywhere.
Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex.
Platform: |
Size: 2751488 |
Author:minu |
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Description: This a software utility. It provides auto attendance management system using face detection technique. Now a day’s many utilities provide face detection system but not intelligent. Our project is more intelligent, cost less and very easy to configure anywhere.
Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex. -This is a software utility. It provides auto attendance management system using face detection technique. Now a day’s many utilities provide face detection system but not intelligent. Our project is more intelligent, cost less and very easy to configure anywhere.
Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex.
Platform: |
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Author:minu |
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Description: 提出了一种基于肤色的精确人脸定位算法,详细叙述了在图片的颜色调整与肤色检测,肤色区域的平滑、
分割与填充,候选眼睛的选取及配对中遇到的具体问题并提出了解决方案. 本算法能较为准确地定位彩色图像
中的正面、小角度偏侧和旋转的人脸,还能检测出一幅图中的多个人脸.-Presents a human face based on skin color precision positioning algorithm, described in detail in the image color adjustment and color detection, color, region, smooth, split with the filling, the candidate selection and matching eyes, the specific problems encountered and suggested a solution program. This algorithm can be more accurately positioned in color images of the positive, unilateral small-angle and rotation of the face, but also detected a diagram of the multi-person face. Platform: |
Size: 222208 |
Author:hope |
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Description: 人脸检测的研究具有重要的学术价值，人脸是一类具有相当复杂的细节变化的自然结构目标，对此类目标的挑战性在于：人脸由于外貌、表情、肤色等不同，具有模式的可变性；一般意义下的人脸上，可能存在眼镜、胡须等附属物；作为三维物体的人脸影像不可避免地受由光照产生的阴影的影响。因此，如果能够找到解决这些问题的方法，成功地构造出人脸检测系统，将为解决其他类似的复杂模式的检测问题提供重要的启示。-Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.
Face detection can be regarded as a more general case of face localization. In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.
Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation), or both. The newer algorithms take into account variations in the image or video by factors such as f Platform: |
Size: 2994176 |
Author:力量 |
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Description: set of action on image , rotation, edge detection,de-noise by DCT,and face recognition by PCA Platform: |
Size: 11264 |
Author:malike |
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Description: 本文考虑带旋转的人脸检测方法, 提出了一种基于颜色空间以及模板匹配的快速人脸定位方法。首先从常用的颜色空间中选
择出对光照因素稳健的肤色子空间, 然后基于该子空间进行肤色检测方法得到人脸大致区域, 最后采用模板匹配的方法确定人脸区域。
实验结果表明, 该方法速度快, 对于带角度旋转的人脸定位有很好的效果。-In this paper, we consider the face detection method with rotating a template matching fast face location method based on color space. First from the common color space is selected the healthy complexion subspace of illumination factors, skin detection method to obtain the approximate area of the face and then on the basis of the sub-space, and finally using the template matching method determines a face region. The experimental results show that this method is faster, and the rotation for the angled face positioning have a good effect. Platform: |
Size: 77824 |
Author:东方 |
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Description: 在解决正面直立人脸检测的基础上，本章进一步研究解决图像平面内任意旋转的正面人脸的检测问题。-On the basis of solving the front upright face detection, this chapter for further research to solve the detection problem frontal arbitrary rotation of the image plane of the face. Platform: |
Size: 917504 |
Author:ch |
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Description: 主方向旋转LBP特征的平面旋转人脸检测,一篇很好的论文，希望大家下载-The main direction of the rotation of the LBP features of the plane rotating face detection Platform: |
Size: 1564672 |
Author:xiaowang |
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Description: 1. 人脸检测的高正确率，误检，漏检很少，支持多脸（max=32）。 平面旋转高达 60 度，并带鼻，嘴定位，及眼镜判断等功能。 2. 支持双目(双摄像头)/多目的 3D 维度识别,增加了人脸的特征集,再次提高识别精度,并有效防止照片通过。 3. 人脸识别的高精度，向用户推荐的识别阀值不仅能适应光线环境的变化，具备满足实际应用的识别正确率。带 眼镜或头发挡住眉毛都行。（但黑粗边眼镜的识别率相对低些，即在较好的识别环境下，黑粗边眼镜仍是 OK 的，只要看得清眼球，就对识别率没有任何影响） 4. 在背光模式下（看上去黑黑的一大片），开启背光识别的开关后，识别速度会减低，把识别阀值调低一些，就 仍然能进行准确的识别。 5. 支持双目，对高清大图，开启二次精定位开关后，能准确地定位人眼中的瞳孔。准确的人眼定位，使 用户能在此基础上开发出多款有创意的延伸产品。(1. Face detection has high accuracy, false detection and few missed detection. It supports multiple faces (max = 32). Plane rotation up to 60 degrees, and with nose, mouth positioning, and glasses judgment and other functions. 2. Support binocular (dual camera) / multi-purpose 3D dimension recognition, increase the feature set of face, improve the recognition accuracy again, and effectively prevent photos from passing. 3. High accuracy of face recognition. The recognition threshold recommended to users can not only adapt to the changes of light environment, but also meet the recognition accuracy of practical application. Wear glasses or hair over your eyebrows. (but the recognition rate of black spectacles is relatively low, that is to say, in a better recognition environment, black spectacles are still OK, as long as you can see the eyeball clearly, there is no impact on the recognition rate.)) Platform: |
Size: 6934528 |
Author:张长宜 |
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Search - rotation face detection