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20160521-16 欧阳书馨:Forensic Facial Sketch Matching: A time-aware study

2016-5-21 22:56| 发布者: 姬艳丽T成电| 查看: 7210| 评论: 0

摘要: 报告嘉宾1:欧阳书馨(伦敦玛丽女王大学(SketchX Lab),北京邮电大学) 报告时间:2016年5月25日(星期三)晚20:00(北京时间) 报告题目:Forensic Facial Sketch Matching: A time-aware study 主持人: 马占宇( ...

报告嘉宾1:欧阳书馨(伦敦玛丽女王大学(SketchX Lab),北京邮电大学)

报告时间:2016525日(星期三)晚20:00(北京时间)

报告题目:Forensic Facial Sketch Matching: A time-aware study

主持人:  马占宇(北京邮电大学)

报告摘要:We investigate whether it is possible to improve the performance of automated facial forensic sketch matching by learning from examples of facial forgetting over time. Forensic facial sketch recognition is a key capability for law enforcement, but remains an unsolved problem. It is extremely challenging because there are three distinct contributors to the domain gap between forensic sketches and photos: The well-studied sketch-photo modality gap, and the less studied gaps due to (i) the forgetting process of the eye-witness and (ii) their inability to elucidate their memory. Unlike previous studied solving all three factors by one model, we tried to tackle this problem by modeling those factors with different models. We introduced a database of 400 forensic sketches created at different time-delays. Based on this database, we utilize multi-task learning models to model the sketches across time-delays and modalities. We also conducted the experiments with large-mugshot database (10,030). The result outperforms previous method with same scale of data.

参考文献:

[1] ForgetMeNot: Memory-Aware Forensic Facial Sketch Matching, CVPR, 2016

[2] A Survey on Heterogeneous face recognition: sketch, near-infrared, 2D-3D and low-high resolution, IVC, under revision, 2016.

报告人简介:Shuxin Ouyang is a third year PhD student from Queen Mary University of London supervised by Dr. Yi-Zhe Song, Dr. Tim Hospedales and Professor Xueming Li. She have started studying in the SketchX Lab of Queen Mary University of London since September 2014. Her research interests including facial sketch recognition, forensic sketch to mugshot matching, cross-domain modelling and domain-invariant feature learning. She is also interested in machine learning and human computer interaction. Prior to her PhD, she graduated from Beijing University of Posts and Telecommunications with a B.Sc. in Information Engineering in July 2011. She has published several papers including  CVPR, ACCV, etc.

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