Search - facial expression recognition by matlab

Search - facial expression recognition by matlab - List

[Graph Recognize] zhiwenshibie DL : 0: 指纹识别技术是基于生物识别的认证技术，由于每个人的指纹具有唯一性，终身不变，因此指纹识别是代替传统身份识别手段的最安全、最可靠、最方便的方法之一。而指纹的预处理的好坏是指纹识别能否正确识别的关键。-Fingerprint recognition technology is based on biometric authentication technology, as each person
Update : 2025-02-17 Size : 2.54mb Publisher : 张杰
[Graph Recognize] read_image DL : 0: CohnKanada人脸表情库图像读取，经过预处理了的，含有类标，可用于无监督人脸表情识别-images inputing of CohnKanada database,including preprocessing and labels of 7 expressions. it is suitable for unsupervised facial expression recognition
Update : 2025-02-17 Size : 1kb Publisher : bobo
[Software Engineering] kkk DL : 0: 擅蔓：以人脸的表情识别为实验背景，分析了在对人脸表情的识别过程中，单个独立分薰对识别率的影响，由此进一步总结了在表情识别中如何更有效地选取独立子空间，以实现在不影响识别率的前提下，减少用于构成独立子空问所需的独立分量的个数。独立成分分析；表情识别；独立子空间 -Good man: Face to face identification for the experimental background, the analysis of facial expressions in the identification process, a single smoked separate the impact of the recognition rate, thus further summarized in the expression recognition of how to more effectively select the independent sub-space in order to achieve the recognition rate does not affect the premise, for constituting a separate sub-reducing space required for the number of independent component. Independent component analysis expression recognition independent subspace
Update : 2025-02-17 Size : 266kb Publisher : 金振东
[Wavelet] Gabor_signal DL : 1: 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
Update : 2025-02-17 Size : 172kb Publisher : lily
[Other] pcaexpressprot DL : 0: 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.
Update : 2025-02-17 Size : 21kb Publisher : mhm
[matlab] EigenFace DL : 0: 基于PCA的人脸表情识别，可以辨别高兴，愤怒和厌恶三种表情。 -In this project, Eigenfaces are used to classify facial expression. It has been assumed that, facial expression can be classified into some discreet classes (like anger, happiness, disgust or sadness) whereas: 1. Absence of any expression is the "Neutral" expression 2. Intensity of a particular expression can be identified by the level of its "dissimilarity" from the Neutral expression
Update : 2025-02-17 Size : 5.46mb Publisher : 马瑞欣
[2D Graphic] LBPPLPQFER DL : 0: 人脸表情识别matlab程序LBP+LPQ算法融合，SVM分类-facial expression recognition algorithm fusion of LBP and LPQ，clssify by OAA SVM
Update : 2025-02-17 Size : 9.27mb Publisher : xiaxiaoxiao