Spectral Regression dimension reduction for multiple features facial image retrieval

Abstract

Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.