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A New Multimodal Recognition Technique Without Subject's Cooperation Using Neural Network Based Self Organizing Maps

机译:一种新的多模识别技术,没有主题的基于神经网络的自组织地图的合作

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In this manuscript we present a new multimodal biometric system based on neural networks self organizing maps (SOM) for the detection and recognition of face, ear and hand geometry. We use combined principal component analysis (PCA) and SOMs for the dimensionality reduction and then use it for the combined search space optimization of ear, face and hand geometry. We name our method named RJSOM. We show that the proposed RJSOM method improves the performance and robustness of recognition when compared to methods proposed in literature. We apply the proposed method to a variety of datasets and show the results.
机译:在本手稿中,我们介绍了一种基于神经网络的新的多模态生物识别系统,自组织地图(SOM),用于检测和识别脸部,耳朵和手几何形状。我们使用组合主成分分析(PCA)和SOM用于减少维度,然后使用它来实现耳朵,面部和手几何的组合搜索空间优化。我们命名为RJSOM命名的方法。我们表明,与文献中提出的方法相比,建议的RJSOM方法提高了识别的性能和稳健性。我们将建议的方法应用于各种数据集并显示结果。

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