基于并行计算的复合生物测定鉴别
摘要
本文讨论了复合生物测定鉴别系统的建立.生物测定鉴别是自动以生理或者行为性状验证或识别身份的方法.使用单一生物测定的用户验证系统通常会出现充满噪声的传感器数据、有限的自由度、没有普适性的生物测定性状和不可接受的错误率等问题.而改善个体匹配器的性能很难解决上述固有的问题.复合生物测定在另外的角度上增进了整体的性能.此外,由于入侵者很难同时伪造多个生物测定性状,多生物测定系统也具备了一定的反电子欺骗技术.为了合并多个领域专家提供的信息,本文提出了一种跨层次的信息融合方法.针对检测与定位的特点,本文设计了一组结合适应递升算法的具有局部互联结构的神经网络,并且使用并行计算进行加速.文中给出了三个生物测定模态(虹膜、面部、签名)的实验结论.
This paper provides initial results obtained on a multimodal biometric system that uses face,fingerprint and hand geometry features for biometric verification purposes. Our experimentsindicate that the sum rule performs better than the decision tree and linear discriminantclassifiers. The benefits of multibiometrics may become even more evident in the case of alarger database of users. We are, therefore, in the process of collecting data corresponding tothree biometric indicators – iris, face and signature – from a larger user set.Biometric system is defined as "automated methods of verifying or recognizing the identityof a living person on the basis of some physiological characteristic", "or some aspect ofbehavior"
Ross and Jain's experiments in 2003 indicate that the sum rule performs better than thedecision tree and linear discriminant classifiers. User verification systems that use a singlebiometric indicator often have to contend with noisy sensor data, restricted degrees of freedom,non-universality of the biometric trait and unacceptable error rates.
Attempting to improve the performance of individual matchers in such situations may notprove to be effective because of these inherent problems.
Multibiometric systems seek to alleviate some of these drawbacks by providing multipleevidences of the same identity. These systems help achieve an increase in performance that maynot be possible using a single biometric indicator. Further, multibiometric systems provide
anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traitssimultaneously. However, an effective fusion scheme is necessary to combine the informationpresented by multiple domain experts.This dissertation addresses the problem of information fusion in biometric verificationsystems by combining information at the matching score level. Experimental results oncombining three biometric modalities (face, iris, and signature) are presented.
This paper provides initial results obtained on a multimodal biometric system that uses face,fingerprint and hand geometry features for biometric verification purposes. Our experimentsindicate that the sum rule performs better than the decision tree and linear discriminantclassifiers. The benefits of multibiometrics may become even more evident in the case of alarger database of users. We are, therefore, in the process of collecting data corresponding tothree biometric indicators – iris, face and signature – from a larger user set.Biometric system is defined as "automated methods of verifying or recognizing the identityof a living person on the basis of some physiological characteristic", "or some aspect ofbehavior"
Ross and Jain's experiments in 2003 indicate that the sum rule performs better than thedecision tree and linear discriminant classifiers. User verification systems that use a singlebiometric indicator often have to contend with noisy sensor data, restricted degrees of freedom,non-universality of the biometric trait and unacceptable error rates.
Attempting to improve the performance of individual matchers in such situations may notprove to be effective because of these inherent problems.
Multibiometric systems seek to alleviate some of these drawbacks by providing multipleevidences of the same identity. These systems help achieve an increase in performance that maynot be possible using a single biometric indicator. Further, multibiometric systems provide
anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traitssimultaneously. However, an effective fusion scheme is necessary to combine the informationpresented by multiple domain experts.This dissertation addresses the problem of information fusion in biometric verificationsystems by combining information at the matching score level. Experimental results oncombining three biometric modalities (face, iris, and signature) are presented.
引文
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