搜索资源列表
reply_1_897203
- 实现人脸的三维重建，里面ｔｘｔ富有说明 数据保存在ｄａｔａ文件夹中， 用的是ｍｃ算法-Human Face of 3D reconstruction inside txt rich descr iptions of data stored in data files and the algorithm is mc
123569
- 3D/3D+2D人脸识别算法的优势的一篇综述论文 Advances and challenges in 3D and 2D+3D human face recognition-3D/3D+ 2D face recognition algorithm is an overview paper advantage Advances and challenges in 3D and 2D+ 3D human face recognition
3D
- 基于激光扫描的人脸软组织三维重建研究,采用激光对人脸进行扫描!-Based on laser scanning of the human face three-dimensional reconstruction of soft tissue using laser scanning for facial!
face_detection
- computer vision, automatic detection, human face, face candidates search, skin-colour determination, 2D colour space, 3D colour space, illumination independence.
3D_Human_Face_Modeling_Based_On_Photos
- 基于照片的三维人脸建模 。 本文讨论了基于照片的三维人脸建模技术的过程，并且简要地分析了各种常用算法的优点、缺点以及该领域目前的发展状况和所面临的挑战。通过阅读本文，读者们可以全面地了解基于照片的三维人脸建模的过程和常用的方法。-In this paper, we discuss the procedure of 3D human face modeling based of photos, and analyze many typical methods in this area brie
Surf3D
- a real-time 3D pointing gesture recognition algorithm for natural human-robot interaction (HRI). The recognition errors in previous pointing gesture recognition algorithms are mainly caused by the low performance of the hands tracking module
REAAL
- real-time 3D pointing gesture recognition algorithm for natural human-robot interaction (HRI). The recognition errors in previous pointing gesture recognition algorithms are mainly caused by the low performance of the hands tracking module an
source
- html5做的3d的人脸动画，人脸可以用鼠标拖动，可以分辨出人嘴和眼睛-html5 do 3d animation human face, human face can be dragged with the mouse, you can tell in the mouth and eyes
3d--virtual-human-
- 通用的3d人体模型，文件格式是.3ds，可导入vc中，进行三维人脸建模使用，省去在vc中三维建模的麻烦- 3d human body model in a common file formats .3 ds, import vc 3D face modeling, eliminating the trouble in the three-dimensional modeling vc
Face_Angle_Neural_net
- 人面部三维下图像比较，matlab环境下仿真实例，-The human face 3D images under Matlab environment simulation examples
DeepFace
- DeepFace一文依旧是沿着“检测-对齐-人脸表示-分类”这一人脸识别技术路线来的，其贡献在于对人脸对齐和人脸表示环节的改进。1）在人脸对齐环节，引入了3D人脸模型对有姿态的人脸就行分片的仿射对齐。2）在人脸表示环节，利用一个9层的深度卷积在包含4000人、400万张人脸的数据集上学习人脸表示，这个9层的DCNN网络有超过1.2亿个参数。本文的模型在LFW数据集上取得了97.25 的平均精度（逼近了人类97.5 的极限），同时在Youtube数据集上取得了当前最好的结果，比之前的NO.1整整高