Description: 在三维人脸建模中特征点的提取方法，文章中对该方法进行详尽的描绘，对做三维图像来说是很好的参考资料-Face in the three-dimensional modeling of the feature point extraction method, article, the method described in detail on the three-dimensional image is to do a very good reference Platform: |
Size: 234496 |
Author:jack |
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Description: 通过图像预处理，实现人脸边缘检测，特征点提取。标记出眼、鼻、嘴三处特征点-Through preprocessing, for face detection, feature point extraction. Mark out the eyes, nose, mouth and three feature points Platform: |
Size: 7212032 |
Author:ql |
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Description: 人脸检测与特征定位，先做完人脸检测才能标注特征，在人脸检测区域可以进行二值化图像，相似度计算等操作，在特征标注区可以完成边缘提取，标记人脸器官操作-Face detection and feature location, perfect face detection to do first annotation feature, the face detection area in the binarized image can be performed, similarity calculation operations can be completed in the feature gutter edge extraction, operation mark Face Organ Platform: |
Size: 398336 |
Author:DK_pis |
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Description: 直方图提取函数，用于人脸识别中，输入一幅图像，函数内部对输入图像做短时傅里叶变换，得到四幅幅度图，再求四幅图的直方图特征，合并后输出-Histogram extraction functions for face recognition, the input image, the input image inside the function to do short-time Fourier transform, the four magnitude diagram, and then seek four graph histogram feature, the combined output Platform: |
Size: 3301376 |
Author:dd |
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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: OpenCV实现人脸识别功能。采用基本的算法实现特征点提取和匹配。-OpenCV face recognition function. The basic algorithm uses the feature point extraction and matching. Platform: |
Size: 3648512 |
Author:liuming |
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Description: 这个是面部表情自动识别的源代码。它是基于Viola和Jones的面部检测算法和Gabor面部特征点的提取，然后使用训练过的神经网络来识别人脸表情。-This is the automatic recognition of facial expression source code. It is Viola and Jones face detection algorithms and Gabor facial feature point extraction, and then use the trained neural network based on facial expression recognition. Platform: |
Size: 14336 |
Author:彭格格 |
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Description: 这是loggabor的核心代码，主要实现的是对人脸的特征提取。(This is the core loggabor code, the main achievement is the extraction of the face of the feature.) Platform: |
Size: 1024 |
Author:AmandaRuan |
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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 |
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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 |
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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 - face feature extraction