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Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis

机译：基于人脸识别的生物特征识别的模式识别与验证系统分析研究

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Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
机译：人脸识别长期以来一直是计算机视觉的目标，但是直到最近几年，可靠的自动人脸识别才成为生物识别研究的现实目标。本文研究了分类器分析对人脸生物特征验证性能的贡献。它指的是在分类任务中使用多个观察值及其在数据上的明智融合的范例，因此不同级别的决策融合可提高正确的决策性能。这项工作中报告的融合任务是通过融合两个著名的面部识别器ICA I和ICA II来完成的。它结合了匹配分数级别的决策，采用了一种新颖的融合策略;得分内的可能性比融合。这种策略提高了人脸识别系统的准确性，同时减少了个人识别器的局限性。在三个不同的人脸数据库ORL 94，印度人脸数据库和eNTERFACE2005动态人脸数据库的基础上对分析研究的性能进行了测试，仿真结果显示出了显着的性能成就。

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