Lu Guanming

Orthogonal isometric projection for face recognition

Abstract Isometric projection (IsoProjection) is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality. The non-orthogonality also distorts the metric structure embedded in the data. This paper proposes a new method called orthogonal isometric projection (O-IsoProjection), which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection. Similar to IsoProjection, O-IsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space, and then obtains the projections by preserving such a graph structure. Different from IsoProjection, O-IsoProjection requires the basis vectors to be orthogonal, and the orthogonal basis vectors can be calculated by iterative way. Experimental results on ORL and Yale databases show that O-IsoProjection has better recognition rate for face recognition than Eigenface, Fisherface and IsoProjection.

Speaker conversion using kernel non-negative matrix factorization

Abstract Voice conversion (VC) based on Gaussian mixture model (GMM) is the most classic and common method which converts the source spectrum to target spectrum. However this method is prone to over-fitting because of its frame-by-frame conversion. The VC with non-negative matrix factorization (NMF) is presented in this paper, which can keep spectrum from over-fitting by adjusting the size of basis vector (dictionary). In order to realize the non-linear mapping better, kernel NMF (KNMF) is adopted to achieve spectrum mapping. In addition, to increase the accuracy of conversion, KNMF combined with GMM (GKNMF) is also introduced into VC. In the end, KNMF, GKNMF, GMM, principal component regression (PCR), PCR combined with GMM (GPCR), partial least square regression (PLSR), NMF correlation-based frequency warping (NMF-CFW) and deep neural network (DNN) methods are compared with each other. The proposed GKNMF gets better performance in both objective evaluation and subjective evaluation.

Motion-based region growing segmentation of image sequences

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pre- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.