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Fusion of intra- and inter-modality algorithms for face-sketch recognition

机译:融合模态内和模态算法以进行人脸识别

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摘要

Identifying and apprehending suspects by matching sketches created from eyewitness and victim descriptions to mugshot photos is a slow process since law enforcement agencies lack automated methods to perform this task. This paper attempts to tackle this problem by combining Eigentransformation, a global intra-modality approach, with the Eigenpatches local intra-modality technique. These algorithms are then fused with an inter-modality method called Histogram of Averaged Orientation Gradients (HAOG). Simulation results reveal that the intra- and inter- modality algorithms considered in this work provide complementary information since not only does fusion of the global and local intra-modality methods yield better performance than either of the algorithms individually, but fusion with the inter-modality approach yields further improvement to achieve retrieval rates of 94.05% at Rank-100 on 420 photo-sketch pairs. This performance is achieved at Rank-25 when filtering of the gallery using demographic information is carried out.
机译:通过将目击者和受害者描述中创建的草图与面部照片相匹配来识别和逮捕嫌疑犯是一个缓慢的过程,因为执法机构缺乏自动的方法来执行此任务。本文试图通过将本征变换(一种全局的内部模态方法)与本征补丁局部局部模态技术相结合来解决此问题。然后,将这些算法与称为平均定向梯度直方图(HAOG)的互模方法融合。仿真结果表明,本文中考虑的模态和模态算法提供了补充信息,因为不仅全局和局部模态内方法的融合比单个算法都具有更好的性能,而且与模态内的融合该方法产生了进一步的改进,以实现对420个光素描对在Rank-100上的94.05%的检索率。使用人口统计信息对图库进行过滤时,该性能在Rank-25处达到。

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