Description: 这是一个nust人脸数据库,对做模式识别的人来说非常有用的库.-This is a nust face databases, pattern recognition on people who make very useful library. Platform: |
Size: 3156992 |
Author:liuhua |
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Description: TI最新的davinci处理器介绍及datasheet,包括应用的领域,最新支持人脸识别技术.-TI introduced the latest and davinci processor datasheet, application areas, including the latest technology to support face recognition. Platform: |
Size: 4025344 |
Author:ouwei |
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Description: Library of Congress Cataloging-in-Publication Data
Handbook of face recognition / editors, Stan Z. Li & Anil K. Jain.
p. cm.
Includes bibliographical references and index.-Library of Congress Cataloging-in-Publication Data
Handbook of face recognition/editors, Stan Z. Li & Anil K. Jain.
p. cm.
Includes bibliographical references and index. Platform: |
Size: 12648448 |
Author:seven |
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Description: 三维人脸识别的经典文章，是三维人脸表情识别论文争相引用的经典文章-Using Biologically Inspired Features for Face Processing
In this paper, we show that a new set of visual
features, derived from a feed-forward model of the primate
visual object recognition pathway proposed by Riesenhuber
and Poggio (R&P Model) is capable of matching the performance of
some of the best current representations for face identification
and facial expression recognition Platform: |
Size: 384000 |
Author:浪子 |
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Description: 基于形态学商图像的光照归一化算法.复杂光照条件下的人脸／P,~J1是一个困难但需迫切解决的问题，为此提出了一种有效的光照归一化算法．
该方法根据面部光照特点，基于数学形态学和商图像技术对各种光照条件下的人脸图像进行归一化处理，并且将它
发展到动态地估计光照强度，进一步增强消除光照和保留特征的效果．与传统的技术相比，该方法无须训练数据集以
及假定光源位置，并且每人只需一幅注册图像，在耶鲁人脸图像库B上的测试表明，该算法以较小的计算代价取得了
优良的识别性能．-Face recognition under complex illumination conditions is still an open question．To cope with the
problem ，this paper proposes an effective illumination normalization method．The proposed method employs
morphology and quotient image techniques by analyzing the face illumination，and it is upgraded with dynamical
lighting estimation technique to strengthen illumination compensation and feature enhancement．Compared with
traditional approaches，this method doesn’t need any training data and any assumption on the light conditions，
moreover，the enrollment requires only one image for each subject．The proposed methods are evaluated on Yale
Face database B and receive a very competitive recognition rate with low computational cost． Platform: |
Size: 299008 |
Author:郭事业 |
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Description: 在图像处理特别是人脸识别中经常用到PCA算法，这是基于Opencv的PCA算法。-In the image processing in particular are often used in PCA face recognition algorithm, which is based on the Opencv the PCA algorithm. Platform: |
Size: 1024 |
Author:liwei |
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Description: Performs Procustes point alignment on a group of point sets. Method rigidly aligns, shifts, and scales points to reduce mean square error.
Method is described in:
B. Klare, P Mallapragada, A.K. Jain, and K. Davis, "Clustering Face Carvings: Exploring the Devatas of Angkor Wat", in Proceedings International Conference on Pattern Recognition (ICPR), 2010.
http://www.cse.msu.edu/~klarebre/docs/ICPR_AW.pdf-Performs Procustes point alignment on a group of point sets. Method rigidly aligns, shifts, and scales points to reduce mean square error.
Method is described in:
B. Klare, P Mallapragada, A.K. Jain, and K. Davis, "Clustering Face Carvings: Exploring the Devatas of Angkor Wat", in Proceedings International Conference on Pattern Recognition (ICPR), 2010.
http://www.cse.msu.edu/~klarebre/docs/ICPR_AW.pdf Platform: |
Size: 1024 |
Author:B |
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Description: The 3-D Morphable Model was
introduced as a generative model to p redictthe appearances o f
an individual while using a statistical prior on shape and
texture allowin g its parameters to be estimated from single
image. Based on these new unde rstandings , face recognition
algorithms have been developed to address the joint challenges of pose and lighting. Platform: |
Size: 1209344 |
Author:bobobobo |
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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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