Sidney Sussex College, Department of Engineering, University of Cambridge, Cambridge, UK.
IEEE Trans Image Process. 2010 Apr;19(4):1067-74. doi: 10.1109/TIP.2009.2038621. Epub 2009 Dec 15.
We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.
我们通过匹配图像集来解决人脸识别问题。每一组人脸图像都由一个子空间（或线性流形）表示，识别是通过子空间到子空间的匹配来进行的。在本文中，1）提出了一种新的判别方法，该方法通过最大化不同类之间的角度来最大化子空间之间的正交性。2）我们提出了一种在线更新判别子空间的方法，作为一种持续提高识别精度的机制。3）提出了一种称为局部正交子空间方法的进一步增强方法，以最大化竞争类之间的正交性。使用 700 个人脸图像集的实验表明，所提出的方法优于相关的现有技术，并通过在线学习有效地提高了识别精度。结果表明，在线学习方法以远低于批处理计算的成本提供了相同的解决方案，并且局部正交方法显示出了更高的准确性。我们还在多个生物识别大挑战的门户场景中展示了所提出的人脸识别方法的优势。
