Multi wavelet Feature Extraction Method for Variable Illumination Face Recognition
A face recognition method based on the logarithm restoration algorithm and the multi wavelet LH features is proposed in this paper to solve the variable illumination problem. By analyzing the model of variable illumination face images, all multi wavelet frequency band features and their frequency amplitudes of the preprocessed image by the logarithm and normalization computation, it can be seen that the multi wavelet LH features can present the detail features of the face image better. Thus, the face image is calculated by the logarithm and normalization operations firstly, which change the multiplicative noise to the additive noise and can reduce the illumination effects effectively. And then the multi wavelet LH features are obtained by filtering every row and every column of the preprocessed image using the multi wavelet low pass filter and the multiwavelet high pass filter sequentially. At last, the test face image is identified using the nearest neighborhood method. The experimental results based on the extended Yale B and CMU PIE Pose C09 databases show that the proposed method can obviously improve the face recognition effectiveness with a good robustness.
J. Inf. Hiding Multim. Signal Process.