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． Platform: |
Size: 205824 |
Author:费富里 |
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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. Platform: |
Size: 21504 |
Author:mhm |
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Description: Along with biological features recognition technology s development，the
high precision biometric technology is widely used in the identity verification
more and more．Such as：fingerprint recognitions，iris scanning，retina
scanning，sound ripple，palm prints，facial features recognition and so
on．The technologies including fingerprints，retina，iris biometric recognition
have relatively high credibility and accuracy．Because fingerprint gathering is
relatively convenient and the hardware is easy to be realized，the fingerprint
recognition algorithm is more mature than the other biometric recognition
technologies．The fingerprint recognition has higher usability and feasibility in
terms of the overall performances．-Along with biological features recognition technology' s development, the high precision biometric technology is widely used in the identity verification more and more. Such as: fingerprint recognitions, iris scanning, retina scanning, sound ripple, palm prints, facial features recognition and so on. The technologies including fingerprints, retina, iris biometric recognition have relatively high credibility and accuracy. Because fingerprint gathering is relatively convenient and the hardware is easy to be realized, the fingerprint recognition algorithm is more mature than the other biometric recognition technologies. The fingerprint recognition has higher usability and feasibility in terms of the overall performances. Platform: |
Size: 540672 |
Author:zhengxinlong |
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Description: 本文在Matlab R2012a下面实现了fisherface算法，选用的人脸库是ORL，其中有40 人，每人有10幅不同的人脸图像。本文选取了每人9幅作为训练(1幅作为测 试)，图像大小为112x92。
主程序入口：Main.m
读取样本：CreatDatabase.m
FisherFace核心：FisherFaceCore.m
识别：Recognition.m
训练样本库：TrainDatabase
测试样本库：TestDatabase
不足：识别准确率有待提高。-In this paper, Matlab R2012a achieved fisherface following algorithm is chosen face database ORL, in which 40 people, each person will have 10 different facial images. This paper selected as the training 9 per person (one as a test), the image size is 112x92.
Main entrance: Main.m
Read sample: CreatDatabase.m
FisherFace Core: FisherFaceCore.m
Identification: Recognition.m
Training sample library: TrainDatabase
Test sample library: TestDatabase
Inadequate: recognition accuracy to be improved. Platform: |
Size: 4238336 |
Author:邱竞 |
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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: |
Size: 20067328 |
Author:季科 |
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Search - facial recognition algorithm in matlab