Description: 我的高维LBP特征的人脸识别方法董晨，徐东曺，方文执行，简隼。维度祝福：高维特征及其对人脸验证的有效压缩。计算机视觉与模式识别（CVPR），2013。我用OpenCV的人脸检测和人脸地标检测内部接口。实现的详细信息可以在Bor Chun Chen、楚松晨、Winston H. Hsu中找到。审查和高维局部二进制模式及其在人脸识别中的应用实施，技术报告tr-iis-14-003，中央研究院信息科学研究所，2014。（PDF）如果您使用此代码进行研究，请引用上面的技术报告。有关更多信息，请访问项目网站# #前提# # # OpenCV(My implementation of high dimensional lbp feature for face recognition based on Dong Chen, Xudong Cao, Fang Wen, Jian Sun. Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification. Computer Vision and Pattern Recognition (CVPR), 2013. I use openCV for face detection and IntraFace for facial landmark detection. Details of the implementations can be found in Bor-Chun Chen, Chu-Song Chen, Winston H. Hsu. Review and Implementation of High-Dimensional Local Binary Patterns and Its Application to Face Recognition, Technical Report TR-IIS-14-003, Institute of Information Science, Academia Sinica, 2014. (PDF) If you use this code for your research, please kindly cite the technical report above. For more information, pelase visit the project website ##Prerequisites ###openCV) Platform: |
Size: 336896 |
Author:moonteresa
|
Hits:
Description: 非常经典的特征提取算法，经常用来做降维方法，但是也可以直接用来做特征提取，很适合图像处理入门，在人脸识别也经常用到(Very classic feature extraction algorithm, often used to do dimensionality reduction methods, but can also be used directly to do feature extraction, it is suitable for image processing, in face recognition is often used) Platform: |
Size: 6807552 |
Author:firefly1
|
Hits:
Description: 对人脸进行特征提取，并使用K近邻算法得其识别率(Feature extraction is performed on the face and its recognition rate is obtained using K nearest neighbor algorithm) Platform: |
Size: 1024 |
Author:冰糖雪梨额
|
Hits:
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 |
Hits:
Description: 使用稀疏特征算法实现人脸识别，带有例程，基本实现人脸识别(Using sparse feature algorithm to realize face recognition with routineBasic realization of face recognition) Platform: |
Size: 3829760 |
Author:问问百合大法 |
Hits:
Description: 主流的人脸识别技术基本上可以归结为三类，即：基于几何特征的方法、基于模板的方法和基于模型的方法。
1. 基于几何特征的方法是最早、最传统的方法，通常需要和其他算法结合才能有比较好的效果；
2. 基于模板的方法可以分为基于相关匹配的方法、特征脸方法、线性判别分析方法、奇异值分解方法、神经网络方法、动态连接匹配方法等。
3. 基于模型的方法则有基于隐马尔柯夫模型，主动形状模型和主动外观模型的方法等。(The mainstream face recognition technology can be basically summed up in three categories, namely, the geometric feature based method, the template based method and the model based method.
1. the geometric feature based method is the earliest and most traditional method, and it usually needs to be combined with other algorithms in order to have a better effect.
2., template based methods can be divided into related matching methods, eigenfaces, linear discriminant analysis, singular value decomposition, neural network and dynamic link matching.
3. the model based approach is based on the implicit Markov model, the active shape model and the active appearance model.) Platform: |
Size: 16384 |
Author:哈哈4 |
Hits:
Description: 这篇文章是撸主要介绍人脸识别经典方法的第一篇，后续会有其他方法更新。特征脸方法基本是将人脸识别推向真正可用的第一种方法，了解一下还是很有必要的(This article is the first line mainly introduces classical face recognition methods, the follow-up there will be other methods to update. The feature face method is basically the first way to make face recognition really available. It is still necessary to understand it.) Platform: |
Size: 150528 |
Author:哈哈4 |
Hits:
Description: 将理论知识、科学研究和工程实践有机结合起来，内容涉及数字图像处理和识别技术的方方面面，包括图像的点运算、几何变换、空域和频域滤波、小波变换、图像复原、形态学处理、图像分割以及图像特征提取的相关内容；同时对于机器视觉进行了前导性的探究，重点介绍了两种目前在工程技术领域非常流行的分类技术——人工神经网络（ANN）和支持向量机（SVM），并在人脸识别这样的热点问题中结束《精通Matlab数字图像处理与识别》。(Combining theoretical knowledge, scientific research and engineering practice, the content involves all aspects of digital image processing and recognition technology, including point operation, geometric transformation, spatial and frequency domain filtering, wavelet transform, image restoration, morphological processing, image segmentation and image feature extraction, and the exploration of the preamble of machine vision. Two kinds of classification techniques, artificial neural network (ANN) and support vector machine (SVM), which are popular in the field of engineering and technology, are introduced in this paper, and the "master Matlab digital image processing and recognition" is finished in the hot issues such as face recognition.) Platform: |
Size: 17900544 |
Author:Xingli |
Hits:
Description: 主要功能为脸部识别，有相关分类器标记特征点之间的距离(The main function is face recognition, and the distance between feature points with related classifier marks.) Platform: |
Size: 71682048 |
Author:muzine1995 |
Hits:
Description: 人脸识别完整程序，包含人脸检测与定位，特征提取与人脸识别环节(Face recognition complete program, including face detection and localization, feature extraction and face recognition.) Platform: |
Size: 105472 |
Author:nabaobao |
Hits:
Description: 人脸特征提取matlab源码。适用于人脸识别的matlab实现。(Facial feature extraction matlab source code. It is suitable for matlab implementation of face recognition.) Platform: |
Size: 3875840 |
Author:ZCzhuang |
Hits:
Description: 1. 人脸检测的高正确率，误检，漏检很少，支持多脸（max=32）。 平面旋转高达 60 度，并带鼻，嘴定位，及眼镜判断等功能。 2. 支持双目(双摄像头)/多目的 3D 维度识别,增加了人脸的特征集,再次提高识别精度,并有效防止照片通过。 3. 人脸识别的高精度，向用户推荐的识别阀值不仅能适应光线环境的变化，具备满足实际应用的识别正确率。带 眼镜或头发挡住眉毛都行。（但黑粗边眼镜的识别率相对低些，即在较好的识别环境下，黑粗边眼镜仍是 OK 的，只要看得清眼球，就对识别率没有任何影响） 4. 在背光模式下（看上去黑黑的一大片），开启背光识别的开关后，识别速度会减低，把识别阀值调低一些，就 仍然能进行准确的识别。 5. 支持双目，对高清大图，开启二次精定位开关后，能准确地定位人眼中的瞳孔。准确的人眼定位，使 用户能在此基础上开发出多款有创意的延伸产品。(1. Face detection has high accuracy, false detection and few missed detection. It supports multiple faces (max = 32). Plane rotation up to 60 degrees, and with nose, mouth positioning, and glasses judgment and other functions. 2. Support binocular (dual camera) / multi-purpose 3D dimension recognition, increase the feature set of face, improve the recognition accuracy again, and effectively prevent photos from passing. 3. High accuracy of face recognition. The recognition threshold recommended to users can not only adapt to the changes of light environment, but also meet the recognition accuracy of practical application. Wear glasses or hair over your eyebrows. (but the recognition rate of black spectacles is relatively low, that is to say, in a better recognition environment, black spectacles are still OK, as long as you can see the eyeball clearly, there is no impact on the recognition rate.)) Platform: |
Size: 6934528 |
Author:张长宜 |
Hits:
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 |
Hits:
Search - face feature recognition