Illumination invariant face recognition based on the new phase features

Hilbert-Huang transform (HHT) is a novel signal processing method which can efficiently handle non-stationary and nonlinear signals. Two key parts are included: 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 the original signals. Hilbert transform is adopted on the IMFs to get the analytical local features. Due to its efficiency in signal processing, the bidimensional version has been studied for the advanced image processing. EMD has been extended to bidimensional EMD (BEMD), and 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. The quantity Phase congruency (PC) is invariant to changes in image illumination. In this paper, we firstly proposed an improved BEMD method based on the novel evaluation of local mean, then the Riesz transform is applied to get the corresponding monogenic signals. Finally, PC was calculated based on the new phase information and it then has been adopted as facial features to classify faces under variant illumination conditions. The experimental results demonstrated the efficiency of the proposed approach.