Flexible images: matching and recognition using learned deformations
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Image Feature Extraction Based on the Two-Dimensional Empirical Mode Decomposition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
An Image Watermarking Method Based on Bidimensional Empirical Mode Decomposition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05
Multiresolution face recognition
Image and Vision Computing
The application of neural network and wavelet in human face illumination compensation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
| Hi-index | 0.01 |
Two Empirical Mode Decomposition (EMD) based face recognition schemes are proposed in this paper to address variant illumination problem. EMD is a data-driven analysis method for nonlinear and non-stationary signals. It decomposes signals into a set of Intrinsic Mode Functions (IMFs) that containing multiscale features. The features are representative and especially efficient in capturing high-frequency information. The advantages of EMD accord well with the requirements of face recognition under variant illuminations. Earlier studies show that only the low-frequency component is sensitive to illumination changes, it indicates that the corresponding high-frequency components are more robust to the illumination changes. Therefore, two face recognition schemes based on the IMFs are generated. One is using the high-frequency IMFs directly for classification. The other one is based on the synthesized face images fused by high-frequency IMFs. The experimental results on the PIE database verify the efficiency of the proposed methods.