EDA-based optimal Gabor kernel's scale and orientation selection for facial expression recognition

摘要

In order to reduce Gabor features dimension and remove redundant information between features, we propose a new Gabor features dimension reduction method that utilizes estimation of distribution algorithms (EDA) to search optimal Gabor kernels' scales and orientations. We equate feature dimension reduction problem to optimal Gabor kernel's scales and orientations selection problem. This method is applied to facial expression recognition. Experimental results on JAFFE database demonstrate that our method is more effective for both dimension reduction and image representation than traditional Gabor filter bank.