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Transfer Learning Based Evolutionary Algorithm for Composite Face Sketch Recognition

机译：基于转移学习的复合脸草图识别进化算法

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摘要

Matching facial sketches to digital face images has widespread application in law enforcement scenarios. Recent advancements in technology have led to the availability of sketch generation tools, minimizing the requirement of a sketch artist. While these sketches have helped in manual authentication, matching composite sketches with digital mugshot photos automatically show high modality gap. This research aims to address the task of matching a composite face sketch image to digital images by proposing a transfer learning based evolutionary algorithm. A new feature descriptor, Histogram of Image Moments, has also been presented for encoding features across modalities. Moreover, IIITD Composite Face Sketch Database of 150 subjects is presented to fill the gap due to limited availability of databases in this problem domain. Experimental evaluation and analysis on the proposed dataset show the effectiveness of the transfer learning approach for performing cross-modality recognition.
机译：与数字面部图像的匹配面部草图在执法方案中具有广泛的应用。技术的最新进步导致了素描生成工具的可用性，最大限度地减少了草图艺术家的要求。虽然这些草图已帮助手动认证，但匹配与数字Mugshot照片的复合草图自动显示高模态间隙。该研究旨在通过提出基于转移学习的进化算法来解决与数字图像匹配到数字图像的任务。还介绍了一个新的特征描述符，图像时刻的直方图，还介绍了跨模式的编码功能。此外，由于在此问题域中的数据库有限，IITD复合面素描数据库为150个受试者的概率填充差距。建议数据集的实验评估和分析显示了跨模型识别的转移学习方法的有效性。

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