Sparse Bayesian reconstruction for optimal facial recognition

稀疏贝叶斯重建以实现最佳面部识别

基本信息

批准号：
ST/T001054/1
负责人：
Anthony Lasenby
金额：
$ 23.12万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FT001054%2F1
关键词：
Sparse Bayesian reconstruction optimal facial

项目摘要

Near 100% accuracy is a necessary condition for facial recognition to become more widely utilised in security and to be more widely accepted by users. Existing techniques in the field of computer vision have taken us to unprecedented levels of accuracy. This project aims to close the final gap in accuracy using cutting-edge image analysis techniques developed by astronomers.Traditional facial recognition software acts directly on a computer processed image to determine an identity. Using a direct image as input has bonuses such as simplicity, but drawbacks in that many theoretical and practical questions are difficult to pose on a pixel-based representation. In a more established image analysis practice, one traditionally acts on a representation of the image that is more specific to the object of study, in this case the human face. In this compressed representation it becomes easier to ask more specific questions.This project seeks to apply the cutting edge of such sparse Bayesian representations to the field of facial recognition, using the state-of-the-art in inference algorithms to determine the optimal and minimal unbiased representation of a human face. This project will be able to provide an orthogonal approach to currently applied techniques and could be the missing link in closing the final gap toward 100% accuracy.

接近100%的准确率是人脸识别在安防领域得到更广泛应用、被用户更广泛接受的必要条件。计算机视觉领域的现有技术已经使我们达到了前所未有的准确性水平。该项目旨在利用天文学家开发的尖端图像分析技术来缩小最终的准确度差距。传统的面部识别软件直接作用于计算机处理的图像来确定身份。使用直接图像作为输入具有简单性等优点，但缺点是许多理论和实践问题很难在基于像素的表示上提出。在更成熟的图像分析实践中，传统上对更具体的研究对象（在本例中为人脸）的图像表示进行操作。在这种压缩表示中，提出更具体的问题变得更加容易。该项目旨在将这种稀疏贝叶斯表示的前沿应用到面部识别领域，使用最先进的推理算法来确定最佳和人脸的最小无偏见表示。该项目将能够为当前应用的技术提供一种正交方法，并可能成为缩小最终达到 100% 准确率差距的缺失环节。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Convergent Bayesian global fits of 4D composite Higgs models

DOI：
10.1007/jhep05(2021)237
发表时间：
2021-01
期刊：
Journal of High Energy Physics
影响因子：
5.4
作者：
E. Carragher;Will Handley;D. Murnane;P. Stangl;W. Su;M. White;Anthony G Williams
通讯作者：
E. Carragher;Will Handley;D. Murnane;P. Stangl;W. Su;M. White;Anthony G Williams

Nested sampling cross-checks using order statistics

使用顺序统计进行嵌套抽样交叉检查