Illumination Invariant Face Recognition based on the New Phase Local Features

Hilbert-Huang transform (HHT) is a novel signal processing method which can efficiently handle nonstationary and nonlinear signals. It contains two key parts: Empirical Mode Decomposition (EMD) and Hilbert transform. EMD decomposes signals into a complete series of Intrinsic Mode Functions (IMFs), which capture the intrinsic frequency components of original signals. Hilbert transform is adopted on the IMFs to get the analytical local features. Recently, bidimensional version has been studied for advanced image processing. EMD has been extended to bidimensional EMD (BEMD), then the corresponding monogenic signals are studied. Phase information is an important local feature of signals in frequency domain because it is robust to contrast, brightness, noise, shading in the image. Phase congruency (PC) is a quantity that is invariant to changes in image illumination. In this paper, we firstly proposed a new BEMD method based on the improved evaluation of local mean, then the Riesz transform is applied to get the corresponding monogenic signals. Finally, PC calculated by the new phase information has been adopted as facial features to classify different faces under variant illumination conditions. The experimental results demonstrate the efficiency of our approach.