On-line learning of mutually orthogonal subspaces for face recognition by image sets.

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.

Chirp limitation on high-speed wavelength conversion using semiconductor optical amplifiers

The cross-gain-saturation effect in SOAs, has been shown to enable robust high-speed wavelength conversion. Under strong electrical and optical pumping, conversion speeds in excess of 20 Gbit/s have been illustrated. However, the effect of chirp on transmission distance at such ultrahigh bit rates has not been studied theoretically in detail. This paper considers the chirp introduced on conversion, employing cross-gain saturation, and studies its dependence on amplifier drive current and signal power. It further shows how an increase in injected cw optical power can reduce chirp while improving conversion speed.