Description: In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.
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Author:Razvan |
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Description: PCA和2DPCA算法由于其在降维和特征提取方面的有效性，在人脸识别领域得到了广泛
的应用。该PPT详细介绍了PCA和2DPCA算法在人脸识别中具体步骤。-PCA and 2DPCA algorithm due to its dimensionality reduction and feature extraction in terms of effectiveness, in face recognition has been widely
Applications. The PPT details of the PCA and 2DPCA specific steps in face recognition algorithm.
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Size: 1372160 |
Author:ryy5745 |
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Description: 人脸识别的一个常见算法，特征脸的提取，基于主成分分析的人脸识别，-A common face recognition algorithm, feature extraction of the face, based on principal component analysis for face recognition Platform: |
Size: 57344 |
Author:蒋峥 |
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Description: 人脸识别程序。算法部分目前分为4个模块：人脸对齐、光照归一化、特征提取和选择、子空间降维，每个模块是一个项目，每个项目生成一个dll供功能部分隐式调用-Recognition program. Part of the algorithm is currently divided into four modules: face alignment, illumination normalization, feature extraction and selection, subspace dimension reduction, each module is a project, each project generates a dll called implicitly for the functional part Platform: |
Size: 1838080 |
Author:蔡梦佳 |
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Description: 一个外国研究生的毕设，做的是一个用通过改善ASM算法来实现更精准的人脸识别和特征提取-A complete set of foreign graduate students, is a done through improved ASM algorithm to achieve more precise face recognition and feature extraction Platform: |
Size: 16081920 |
Author:王开荣 |
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Description: Nowadays security becomes a most important issue regarding a spoof attack. So, multimodal biometrics technology has attracted
substantial interest for its highest user acceptance, high security, high accuracy, low spoof attack and high recognition
performance in biometric recognition system. This multimodal biometrics system introduces recognition of person from two
things i.e. face & palm print. Principal Component Analysis (PCA) algorithm is used for reduction of dimension & extraction of
features in terms of eigenvalues & eigenvectors. Feature level fusion technique used to fuse the results of face & palm prints and
then gives the output as per neural network classifier which gives the correct information about genuine or imposter identity. Platform: |
Size: 282624 |
Author:atish |
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Description: 基于Yale-B和CMU-PIE 人脸库上的实验结果显示本文算法对复杂光照具有较强鲁棒性，具备提取复杂光照条件下人脸图像有效特征的能力。小波变换 特征提取 matlab仿真-Experimental results based on Yale-B and CMU-PIE face show that the proposed algorithm has a strong light on the complexity of robustness, with the extraction of complex lighting conditions in human face images effective feature capabilities. Wavelet transform feature extraction matlab simulation Platform: |
Size: 390144 |
Author:suca |
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Description: PCA算法的人脸识别代码，主要实现特征脸的提取和人脸的识别。-PCA face recognition algorithm code, the main achievement of face recognition feature extraction and face. Platform: |
Size: 1024 |
Author:朱希成 |
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Description: This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database-This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database Platform: |
Size: 177152 |
Author:Jashpreet |
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Description: This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database-This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database Platform: |
Size: 1664000 |
Author:Jashpreet |
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Description: 局部多值模式LMP，对lbp的改进算法，用于人脸识别局部文理特征的提取-
Partial multiple- valued model LMP, improvements to the LBP algorithm for Face Recognition Feature extraction of local arts and sciences Platform: |
Size: 16384 |
Author:杜跃伟 |
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Description: 人脸识别技术作为生物体特征识别技术的重要组成部分，在近些年来已经发展成为计算机视觉和模式识别领域的研究热点。本实验是基于K-L变换的主成分分析法（PCA）在人脸识别中的应用，在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真，并对样本图像进行了重构。本实验在ORL人脸库的基础上，选用每人前5张图片，共计40人200幅样本图像，通过快速PCA算法将10304维的样本特征向量降至20维，并实现了基于主分量的人脸重建，验证了PCA算法在高维数据降维处理与特征提取方面的有效性。-Facial recognition technology as a biological feature recognition technology is an important part of, in recent years has become a hot research topic in the field of computer vision and pattern recognition.This experiment is based on K- L transform principal component analysis (PCA) in the application of face recognition, based on ORL face validation of rapid PCA algorithm was realized by Matlab simulation, and reconstructed the sample image., on the basis of the experiment on ORL face , choose top 5 pictures each, a total of 40 people 200 sample image, through rapid PCA algorithm the sample feature vector of 10304 d down to 20 d, and implements the face reconstruction based on principal component, PCA algorithm is verified in the high-dimensional data processing and feature extraction is effective to dimension reduction. Platform: |
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Author:季科 |
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Description: 为了解决车型识别过程中车脸图像特征提取的问题，提出了一种标准化不变矩 算法。在对车脸图像进行分割处理、提取出感兴趣区域、采用轮廓跟踪法得到形状轮廓 的基础上，对原有的 HU 不变矩矩算法进行了标准化处理，并与典型的 HU 不变矩算法 结果进行了对比。实验结果表明，该不变矩标准化算法在对图像进行特征提取方面可 以得到更高精度的特征值。
-In order to solve the model car face image feature extraction in the process of identification, a standardized moment invariant algorithm was presented.Face image segmentation in the car, to extract the interested region, the contour tracking method is adopted to get the shape outline, on the basis of the original HU moment invariant moment algorithm standardized processing, and compares the result with typical HU moment invariant algorithm.The experimental results show that the standardization of moment invariant algorithm on image feature extraction has the higher precision can be obtained by eigenvalue. Platform: |
Size: 177152 |
Author:hahah |
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Description: MATLAB编写的人脸特征提取代码，叫局部保持投影特征提取算法，可以作为人脸识别系统一部分(MATLAB prepared by the facial feature extraction code, called the local projection feature extraction algorithm can be used as part of the face recognition system,feature exaction.) Platform: |
Size: 10240 |
Author:依兰
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Description: 该方法利用人脸具有镜像对称的自然特性，依据奇偶分解原理，生成成镜像奇、偶对称样本，井利用人脸对称图像作为训练样本，再利用主分量分析(PCA)对训练样本进行二阶相关和降维处理，然后对处理后的样本进行ICA特征提取。理论和分析实验证明，该算法有效减线了人脸受到视角、光照、人脸表情、姿势变化等因素的最响，又增加了训练样本容量，减少了计算复杂度，同时有效解决了小样本问题，提高了识别率.(The method uses the natural characteristics of mirror symmetry of human face, generates mirror odd and even symmetrical samples according to the principle of parity decomposition, uses the symmetrical images of human face as training samples, then uses principal component analysis ( PCA ) to carry out second-order correlation and dimensionality reduction processing on the training samples, and then carries out ICA feature extraction on the processed samples. Theoretical and analytical experiments show that the algorithm can effectively reduce the face by the angle of view, lighting, facial expressions, posture changes and other factors, and increase the capacity of training samples, reduce the computational complexity, and effectively solve the problem of small samples, improve the recognition rate.) Platform: |
Size: 4099072 |
Author:廊下青衫 |
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Description: 用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).) Platform: |
Size: 2415616 |
Author:莱尼 |
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Search - Extraction Algorithm of Face Feature