Description: 读入人脸图像，根据图像的水平，垂直灰度积分情况，将人眼范围取出，背景可以较复杂。-Reading into the human face image, according to the image of horizontal, vertical gray points, which will remove the scope of the human eye, the background can be more complicated. Platform: |
Size: 2048 |
Author:大地 |
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Description: 本文介绍了一种灰色图像识别中进行人眼睛定位的算法,首先是根据人脸的水平灰度直方图来确定眼睛的位置,然后,
通过计算眼眶部分像素点的垂直灰度直方图来确定两眼中心,接着从获得的区域中找出一定数量的灰度值小的点,对这些点进行添加、删除和计算,实现眼睛的定位．-In this paper, a gray image recognition in the human eye location algorithm, the first is based on the level of facial histogram to determine the location of the eyes, and then, by calculating the orbital part of the vertical pixel histogram to determine the two Eye Center, and then from the region access to a certain number of gray value to identify a small point, on these points add, delete, and calculated to achieve the positioning of the eyes. Platform: |
Size: 1664000 |
Author:闫慧 |
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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 - eye points in a face