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.

Electroosmotic flow analysis of a branched U-turn nanofluidic device.

In this paper, we present the analysis of electroosmotic flow in a branched -turn nanofluidic device, which we developed for detection and sorting of single molecules. The device, where the channel depth is only 150 nm, is designed to optically detect fluorescence from a volume as small as 270 attolitres (al) with a common wide-field fluorescent setup. We use distilled water as the liquid, in which we dilute 110 nm fluorescent beads employed as tracer-particles. Quantitative imaging is used to characterize the pathlines and velocity distribution of the electroosmotic flow in the device. Due to the device's complex geometry, the electroosmotic flow cannot be solved analytically. Therefore we use numerical flow simulation to model our device. Our results show that the deviation between measured and simulated data can be explained by the measured Brownian motion of the tracer-particles, which was not incorporated in the simulation.