Kim, T-K and Kittler, J and Cipolla, R (2010) On-line learning of mutually orthogonal subspaces for face recognition by image sets. IEEE Trans Image Process, 19. pp. 1067-1074.Full text not available from this repository.
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.
|Uncontrolled Keywords:||Biometric Identification Databases, Factual Discriminant Analysis Face Humans Image Processing, Computer-Assisted Video Recording|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:15|
|Last Modified:||26 Feb 2017 00:14|