2D双树复小波与判别共同矢量结合的人脸认证

针对人脸认证中的小样本问题和Gabor小波特征提取的不足,提出一种有效的人脸认证算法。对预处理后的图像进行2D双树复小波变换,将每幅图像不同尺度下多个方向的小波系数幅值作为特征矢量,表征重要的局部信息;将提取的特征矢量向判别共同矢量空间投影,进一步提取具有判别能力的特征,同时进行降维;根据用户特定阈值进行认证。ORL人脸库和FERET子库上的实验结果验证了算法的有效性。

Fusion of 2D dual-tree complex wavelet transform and discriminative common vector for face verification

To deal with the small sample size problem and overcome disadvantage of Gabor feature extraction in face verification,an efficient algorithm based on the Discriminative Common Vector（DCV） and 2D Dual-Tree Complex Wavelet Transform（2D DT-CWT） is proposed.The 2D DT-CWT is utilized to filter the preprocessed image,and then the amplitude coefficients of different scales and different orientations are lexicographically ordered to form its feature vector.The dimensionality of 2D DT-CWT features is usually high,so features are projected into DCV subspace to reduce the dimensionality and enhance the discriminative ability.Finally,verification is accomplished according to client-specific threshold.The experimental results on ORL database and FERET subset demonstrate the effectiveness of the proposed algorithm