Face recognition based on curvelet and PCA
基于曲波与主分量分析的人脸识别

A new method combining curvelet transform and principal components analysis (PCA) is presented for face recognition. Considering the disadvantage of wavelet, say, it is only optimal in representing point singularities, we use curvelet transform to extract facial features. The facial features being mostly curves, curvelet transform directly takes edges for representation, resulting in a more powerful feature extraction. PCA is then used to map the feature into more meaningful subspace, hence we get higher recognition rate. The experiments demonstrate the effectiveness of the proposed method.