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Description: 人脸识别－仅对静态图片The output of this code is mainly required for feature detection or facial expression recognition purpose.
give the input(name of the image) at the 1st line of the facedetection.m file & run the file.
The face_database folder contain some images on which the code gave favorable output.
-The code will work with the images which don t have much brightness variation on the face.the face must be properly enlighten.
The output of this code is mainly required for feature detection or facial expression recognition purpose.
give the input(name of the image) at the 1st line of the facedetection.m file & run the file.
The face_database folder contain some images on which the code gave favorable output.
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Size: 482304 |
Author: djh |
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Description: 人脸表情识别的Gabor小波变换，最终得到5个尺度，8个方向的不同特征，此程序参考文件Gabor feature classification using enhanced FLD model facial recognition-Facial Expression Recognition of Gabor wavelet transform, finally be five scale, eight directions of different characteristics, this procedure reference Gabor feature classification using enhanced FLD model facial recognition
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Size: 176128 |
Author: lily |
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Description: Real-Time Facial Feature Point Extraction-Localization of facial feature points is an important step for many subsequent facial image analysis tasks. In this paper, we proposed a new coarse-to-fine method for extracting 20 facial feature points from image sequences. In particular, the Viola-Jones face detection method is extended to detect small-scale facial components with wide shape variations, and linear Kalman filters are used to smoothly track the feature points by handling detection errors and head rotations. The proposed method achieved higher than 90 detection rate when tested on the BioID face database and the FG-NET facial expression database. Moreover, our method shows robust performance against the variation of face resolutions and facial expressions.
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Size: 871424 |
Author: Ng Jack |
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Description: A very good paper that explain tracking facial feature under varying face pose and facial expression-This paper presents a hierarchical multi-state pose-dependent approach for facial feature detection and tracking under varying facial expression and face pose. For effective and efficient representation of feature points, a hybrid representation that integrates Gabor wavelets and gray-level profiles is proposed. To model the spatial relations among feature points, a hierarchical statistical face shape model is proposed to characterize both the global shape of human face and the local structural details of each facial component. Furthermore, multi-state local shape models are introduced to deal with shape variations of some facial components under different facial expressions. During detection and tracking, both facial component states and feature point positions, constrained by the hierarchical face shape model, are dynamically estimated using a switching hypothesized measurements (SHM) model. Experimental results demonstrate that the proposed method accurately and robustly tracks faci
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Size: 958464 |
Author: Ng Jack |
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Description: 人脸识别中的坐标校准 内含M FILE 还有两张实验照片-the calibration used in facial detection, including the mfiles and the pics.
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Size: 1222656 |
Author: david |
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Description: 实现了给予人脸的特征提取与模式识别，是模式识别中比较经典的实例分析-Achieved a given facial feature extraction and pattern recognition, is the more classic examples of pattern recognition analysis of
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Size: 1024 |
Author: 黄静 |
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Description: Improved Active Shape Model for Facial Feature
Extraction in Color Images
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Size: 450560 |
Author: 思沃琪 |
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Description: 有关人脸检测的程序，对于给定的图像进行自动人脸检测，挺好用的。-The face detection procedure for a given facial image auto-detection, quite good use.
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Size: 1335296 |
Author: 陈濛 |
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Description: 人脸表情识别中的2DLDA方法，特定人识别。-Facial Expression Recognition in 2DLDA method of identifying a specific person.
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Size: 428032 |
Author: rabbit3306 |
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Description: 人脸表情识别中的2DPCA方法，特定人识别。-Facial Expression Recognition in 2DPCA method of identifying a specific person.
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Size: 428032 |
Author: rabbit3306 |
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Description: 人脸表情识别中的2DPCA方法，非特定人识别。-Facial Expression Recognition in 2DPCA method, non-specific Recognition.
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Size: 428032 |
Author: rabbit3306 |
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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.
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Size: 2751488 |
Author: minu |
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Description: 结合人脸肤色和发色进行的人脸检测，可以忽略人脸的姿态表情，不用进行人脸特征的定位-Combination of face color and hair color for face detection, facial gesture can ignore the expression, do not carry out the positioning of facial features
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Size: 1024 |
Author: gu |
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Description: facial feature recognition
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Size: 3377152 |
Author: maryamsaberi11 |
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Description: 摘要：主成分分析（PCA）的人脸识别算法，以减少的特征向量是涉及到对抽象的特点，改进了主成分分析（一）iUumination算法的变化影响酶原sed.The方法是基于上减低与正常化其相应的标准差的特征向量元素相关联的大特征值的特征向量的影响力的想法。耶鲁大学和耶鲁大学面临的数据库面对数据库B是用来验证-Abstract：In principal component analysis(PCA)algorithms for face recognition，to reduce the influence of the
eigenvectors which relate to the changes of the iUumination on abstract features，a modified PCA ( A)
algorithm is propo sed．The method is based on the idea of reducing the influence of the eigenvectors associated
with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation．
Th e Yale face database and Yale face database B are used to verify the method．The simulation results show
that，f0r front face and even under the condition of limited variation in the facial po ses the proposed method
results in better perform ance than the conventional PCA and linear discriminant analysis(LDA)approaches．and
the computational cost remains the same as that ofthe PCA，and much less than that ofthe LDA．
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Size: 205824 |
Author: 费富里 |
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Description: facial farts tracking in a image using matlab.
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Size: 86016 |
Author: amijeet |
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Description: 三维人脸识别的经典文章，也是三维人脸表情识别引用极多的文章，被模式识别杂志收录-Texture and shape information fusion for facial expression and facial action
unit recognition
Keywords: Facial expression recognition Facial Action Unit recognition Discriminant Non-Negative Matrix Factorization Multidimensional embedding
Support Vector Machines Radial Basis Functions Fusion
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Size: 1350656 |
Author: 浪子 |
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Description: 三维人脸识别的经典文章，三维人脸表情引用次数极多的经典论文-Spontaneous Emotional Facial Expression Detection
Index Terms—affective computing, facial expression, one-class classification, emotion recognition
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Size: 119808 |
Author: 浪子 |
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Description: Facial Expression Recognition using Mahalanobis distance and correlation
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Size: 871424 |
Author: Rahul |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications.
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Size: 21504 |
Author: mhm |
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