中文摘要：

在手掌纹脉图像识别的研究中,多模态融合可以多方面提高生物特征系统的性能。由于错位干扰,掌纹和掌脉的纹理特征编码难以实现双模态同时配准的特征级融合,也难以直接对特征数据进行筛选和压缩。采用离散余弦变换分别提取非接触式掌纹和掌脉特征,避免了匹配配准的平移操作。对特征空间共轭拓展,解决了特征维数和表征形式的兼容性问题。通过特征级融合有效保留了原始鉴别信息,并对特征进行有效归一化处理,实现了分量选择的自适应性。通过提出的自适应共轭鉴别能量分析算法对共轭特征分量进行排序,避免了掩膜窗口的优化问题,筛选出更高鉴别性的分量组合。通过与现有掩膜以及单模态认证方案的对比,验证了手掌纹脉图像特征融合算法在普适性、稳定性和认证精度等方面的有效性。

英文摘要：

Multi-modal fusion can enhance the performance of biometric systems in many ways. Due to the dislo- cation problem, texture feature codes of palmprint and palmvein can be neither used for the dual-modal alignment fu- sion at feature level, nor directly selected or compressed. The features of contactless palmprint and palmvein, which are extracted with discrete cosine transform （ DCT）, do not need to be muhi-translated for matching alignment. The feature space is conjugately expanded to solve the compatibility problem of the dimension and representation of fea- tures. The features are fused to effectively preserve raw discriminant information, and normalized to enhance the a- daptability of coefficient selection. The proposed adaptive conjugate discrimination power analysis （ACDPA） conju- gately orders the feature components, which can avoid the optimization of the pre-masking window, so that the combination of selected feature contains high discrimination. The comparison with the existing pre-masking-based and single-modal algorithms confirms the superiorities of ACDPA in terms of universality, stability and verification accuracy. KEYWORDS：Adaptive conjugate discrimination power analysis; Conjugate expansion of feature space; Contactless palm-print-vein verification