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Description: Abstract
We present a component-based, trainable system for detecting
frontal and near-frontal views of faces in still gray
images. The system consists of a two-level hierarchy of Support
Vector Machine (SVM) classifiers. On the first level,
component classifiers independently detect components of
a face. On the second level, a single classifier checks if the
geometrical configuration of the detected components in the
image matches a geometrical model of a face. We propose
a method for automatically learning components by using
3-D head models. This approach has the advantage that
no manual interaction is required for choosing and extracting
components. Experiments show that the componentbased
system is significantly more robust against rotations
in depth than a comparable system trained on whole face
patterns.
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Size: 349184 |
Author: a |
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Description: 研究描述人脸特征的有效方法, 讨论身份证照片的特征提取和检索采用自适应肤色检测技术改进通用的肤色检测算法, 进行脸部区域的划分提出系数投影法对面部五官区域进行分割, 在各区域中提取面部几何特征引人描述脸颊和下额轮廓的曲线参数作为脸形特征, 得到对人脸特征更准确的描述将面部几何特征矢量匹配、脸形曲线参数匹配和脸部图像相关匹配相结合, 实现人像照片的准确检索实验表明该方法性能优良。-Describe the facial features of an effective way to discuss the ID card photo feature extraction and retrieval using adaptive skin color detection techniques improve the general detection algorithm for facial areas by the proposed coefficient of projection of the facial features to segment the region, in the Extraction of geometric features of the region face cheeks and under the places described in the introduction of profile parameters of the curve as facial features, facial features and get the more accurate description of the feature vector matching facial geometry, face curve parameter matching and matching facial images related to the combination of to achieve an accurate portrait photo retrieval experiments show that the method excellent.
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Size: 699392 |
Author: 郭事业 |
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Description: 本文针对复杂背景下的彩色正面人脸图像,将肤色分割、模板匹配与候选人脸图像块筛选结合起来,构建了人脸检测实验系统,并用自制的人脸图像数据库在该系统下进行了一系列的实验统计。-Color frontal face images under complex background, skin color segmentation, template matching candidates face image block screening combined to build a face detection experimental system using a homemade face image database in the system series of experimental statistics.
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Size: 14336 |
Author: 安静 |
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Description: 本文运用深度神经网络的方法克服姿态变量和图像分辨率的影响,提出了一种多姿态的人脸超分辨识别算法并在实验数据集上获得了良好的性能表现。另外本文利用深度信念网络探索正面人脸图像和侧面人脸图像的映射,方法放松了深度信念网络的输入也输出之间绝对相等,而只是保证其高层含义上的相等。实验表明了使用深度信念网络可以学习到侧面人脸图像到正面人脸图像的一个全局映射,但是丢失了个体细节差异。本文还提出了基于深度网络保持姿态邻域进行姿态分类的方法,在学习过程中,我们保证了同一个姿态下的人脸图像应该与同一姿态下的多张图像互为邻居。实验证明了,我们的方法在用于姿态分类具有非常良好的性能,但是也发现学习过程中,那些与区别个体的信息逐渐丢失了,这也导致了直接运用非线性近邻元分析的特征的人脸识别的性能不佳。-In this paper, the neural network approach to overcome the depth of variables that affect the attitude and image resolution , proposed a multi-pose face recognition algorithms and super-resolution experimental data set obtained in a good performance. Also this paper to explore the depth of belief network mapping frontal face image and profile face images , the method of absolute equality between the input relax depth of belief networks is also output , but only to ensure equal meaning on its top . Experimental results show that the use of deep belief networks can learn to face image to the side of the front face of a global image map , but lost the details of individual differences . This paper also proposes to maintain posture neighborhood depth network-based gesture classification methods in the learning process , we ensure that the face image under the same gesture with multiple images should be under the same attitude are neighbors . Experiment proves that our method for gesture cl
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Size: 9719808 |
Author: cen |
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Description: AR人脸数据库,用于图像检索和图像分类,是BMP格式的,还包括MAT格式.-The AR database consists of over
4000 frontal images from 126 individuals. For each individual,
26 pictures were taken in tow separate sessions
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Size: 3499008 |
Author: Chenxue Yang |
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Description: Frontal views of all subjects are videotaped under
constant illumination using fixed light sources, and none
of the subjects wear eyeglasses. These constraints are
imposed to minimize optical flow degradation. Previously
untrained subjects are video recorded performing a series
of expressions, and the image sequences are coded by
certified FACS coders. Facial expressions are analyzed in
digitized image sequences of arbitrary length (expression
sequences neutral to peak vary 9 to 44 frames).
60 subjects, both male and female, the larger
database were used in this study. The study includes more
than 260 image sequences and 5000 images. Subjects
ranged in age (18-35) and ethnicity (Caucasian, African-
American, and Asian/Indian).-Frontal views of all subjects are videotaped under
constant illumination using fixed light sources, and none
of the subjects wear eyeglasses. These constraints are
imposed to minimize optical flow degradation. Previously
untrained subjects are video recorded performing a series
of expressions, and the image sequences are coded by
certified FACS coders. Facial expressions are analyzed in
digitized image sequences of arbitrary length (expression
sequences neutral to peak vary 9 to 44 frames).
60 subjects, both male and female, the larger
database were used in this study. The study includes more
than 260 image sequences and 5000 images. Subjects
ranged in age (18-35) and ethnicity (Caucasian, African-
American, and Asian/Indian).
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Size: 1024 |
Author: qussai |
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Description: Name: Olivetti - Att - ORL
Color Images: No
Image Size: 92 x 112
Number of unique people: 40
Number of pictures per person: 10
Different Conditions: All frontal and slight tilt of the head
Citation reference: Ferdinando Samaria, Andy Harter. Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994
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Size: 4075520 |
Author: lion_kt
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