摘要： 传统的生物特征识别系统依靠单一来源的生物特征信息完成对象鉴别,但是光照变化、噪声和遮挡等因素对生物特征信息的污染会使其识别性能降低。为此,提出一种多模态稀疏表示算法。在使测试对象不同模态的观测值共享稀疏表示的情况下,用训练数据的稀疏线性组合表示测试数据。算法的优化问题通过一种高效的交替方向方法求解。实验结果表明,该算法的识别性能优于基于信息融合的对比方法。

关键词: 稀疏表示, 生物特征识别, 信息融合, 指纹识别, 虹膜识别

Abstract: Traditional biometric recognition system uses single source biometric information for authentication.However,the recognition performance decreases while biometric information is contaminated by various artifacts such as illumination variations,noise,occlusion and so on.A multimodal Sparse Representation(SR) algorithm is proposed,which represents the test data by a sparse linear combination of training data,while enforcing the observations from different modalities of the test subject to share their sparse representations.The optimization problem is solved using an efficient alternative direction method.Experimental results show that the proposed method has better recognition performance than competing method based on information fusion.

Key words: Sparse Representation(SR), biometric recognition, information fusion, fingerprint recognition, iris recognition

中图分类号: 

• TP391.41