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计算机工程 ›› 2011, Vol. 37 ›› Issue (6): 148-150.doi: 10.3969/j.issn.1000-3428.2011.06.051

• 人工智能及识别技术 • 上一篇    下一篇

基于Adaboost的人脸与虹膜融合识别

高智英，李 斌   

  1. (中国科学技术大学信息科学技术学院，合肥 230027)
  • 出版日期:2011-03-20 发布日期:2011-03-29
  • 作者简介:高智英(1985－)，女，硕士研究生，主研方向：模式识别；李 斌，副教授
  • 基金项目:
    国家自然科学-广东省联合基金资助重点项目(U08350 02)；教育部-微软重点实验室科研基金资助项目“多媒体计算与通信”(07122808)

Face and Iris Fusion Recognition Based on Adaboost

GAO Zhi-ying, LI Bin   

  1. (College of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China)
  • Online:2011-03-20 Published:2011-03-29

PDF

1382

被引次数

32 | 2

摘要： 传统生物特征识别系统的识别率经常受到环境以及生物学特征的自身局限性影响。针对该不足，提出一种基于人脸与虹膜特征级融合的多模态生物识别系统，采用中心对称局部二值模式算子提取人脸和虹膜的纹理特征，将人脸特征与虹膜特征线性整合成混合特征向量，利用Adaboost算法从该混合特征向量中优选出一组最佳特征组合，从而构成强分类器。实验结果表明，该多模态系统相比单模态系统具有更好的鲁棒性。

关键词: 多模态生物识别, 人脸与虹膜特征融合, 特征描述算子

Abstract: The recognition accuracy of the traditional biometric secure system is often influenced by the environment and physiological, and it leads to the development of the multimodal biometric systems. This paper presents a multi-modal biometric recognition system based on face and iris feature level fusion. Face and iris texture feature extraction is adopted Center-Symmetric Local Binary Pattern(CS-LBP) operators, the feature vectors that are extracted from face and iris are integrated linearly to form a mixed feature vector, and then Adaboost algorithm is used to select and aggregate the effective features from the mixed feature vector to build the strong classifier. Experimental results show that the system has better robustness than the mono-modal system.

Key words: multi-modal biometric recognition, face and iris feature fusion, feature description operator

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