Abstract: Ear and face recognition have occupied important places in biometrics. Drastic pose change, however, has always been an obstacle for their practical application in true life. Therefore this paper proposes a novel method based on strengthened authentication vectors to solve pose problem of ear or face recognition. At the same time, the strengthened ear and face authentication vectors using this method are fused by serial strategy, parallel strategy（General Principal Component Analysis, GPCA） and Canonical Correlation Analysis（CCA） method, and are identified by the nearest neighbor method. Experimental results show that it is remarkably effective for single modal biometrics such as the posed ear or posed face to strengthen authentication vectors, and multimodal biometrics method outperforms conspicuously single modal one.

Key words: authentication vector, multimodal, Kernel Principal Component Analysis（KPCA）, General Principal Component Analysis（GPCA）, Canonical Correlation Analysis（CCA）

摘要： 人耳识别与人脸识别在生物特征识别领域中占有重要位置，然而，剧烈的姿态变化一直是阻碍它们在现实生活中应用的瓶颈，提出一种鉴别矢量增强算法，以解决姿态人耳和姿态人脸图像的识别问题。为了考察多模态识别的可行性和有效性，利用串联、并联（广义主元分析）和典型相关分析等融合策略，将强化后的人耳、人脸鉴别矢量进行有效融合，通过最近邻方法进行分类识别。实验结果表明，鉴别矢量强化算法可以显著提高姿态人耳或是姿态人脸单生物特征的识别率，而多模态方法又会表现出更好的识别性能。

关键词: 鉴别矢量, 多模态, 核主元分析, 广义主元分析, 典型相关分析