The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

本文提出了一种基于t范数的匹配分数融合方法，用于多模式异构生物识别系统。利用掌纹和人脸等生物特征，开发了两种基于特征的多模式识别系统。首先，对掌纹和面部进行预处理，使用相关系数提取特征并计算每个性状的匹配分数，并使用基于t范数的得分水平融合来组合匹配分数。人脸数据库（如人脸94，人脸95，人脸96，FERET，FRGC）和掌纹数据库（如IITD）用于算法的训练和测试。实验结果表明，提出的算法在0.1％的错误接受率（FAR）下提供了99.7％的真实接受率，在0.01％的错误率下提供了99.2％的GAR，大大提高了生物识别系统的准确性。