Sparse Bayesian reconstruction for optimal facial recognition

接近100%的准确率是人脸识别在安全领域得到更广泛应用并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将我们带到了前所未有的准确度水平。该项目旨在利用天文学家开发的尖端图像分析技术缩小最终的精度差距。传统的面部识别软件直接对计算机处理的图像进行操作，以确定身份。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际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，人们传统上对更具体于研究对象的图像的表示进行操作，在这种情况下是人脸。在这种压缩表示中，提出更具体的问题变得更容易。这个项目试图将这种稀疏贝叶斯表示的尖端应用到人脸识别领域，使用最先进的推理算法来确定人脸的最优和最小无偏表示。该项目将能够为目前应用的技术提供一种正交方法，并可能成为缩小最终差距接近100%准确率的缺失一环。

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.