Orthogonal local spline discriminant projection with application to face recognition

In this paper, an efficient feature extraction algorithm called orthogonal local spline discriminant projection (O-LSDP) is proposed for face recognition. Derived from local spline embedding (LSE), O-LSDP not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on several standard face databases demonstrate the effectiveness of the proposed method.

Research highlights
► O-LSDP has a number of desirable properties: O-LSDP computes an explicit linear mapping from the input space to the reduced space. Note that in LSE, the mapping is implicit and it is not clear how new data samples can be embedded.
► O-LSDP attempts to manage the trade-off between MMC, which emphasizes discriminant power, and LSE, which is based mainly on preserving local structure.
► O-LSDP seeks to find a set of orthogonal basis functions and significantly improves its recognition accuracy.