Proceedings of the 2017 Workshop on Recognizing Families In the Wild最新文献

Session details: Session 1: Visual Recognition of Families In the Wild: A Big Data Challenge 会议详情:第一场:野外家庭的视觉识别:大数据挑战

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3247931

Timothy Gillis

引用次数: 0

Recognizing Families In the Wild (RFIW): Data Challenge Workshop in conjunction with ACM MM 2017 识别野外家庭(RFIW):与ACM MM 2017联合举办的数据挑战研讨会

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3134424

Joseph P. Robinson, Ming Shao, Handong Zhao, Yue Wu, Timothy Gillis, Y. Fu

引用次数: 36

Proceedings of the 2017 Workshop on Recognizing Families In the Wild 2017年野外家庭识别研讨会论文集

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421

Y. Fu, Joseph P. Robinson, Ming Shao, Siyu Xia

{"title":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","authors":"Y. Fu, Joseph P. Robinson, Ming Shao, Siyu Xia","doi":"10.1145/3134421","DOIUrl":"https://doi.org/10.1145/3134421","url":null,"abstract":"It is our great pleasure to welcome you to the 2017 Recognizing Families In the Wild (RFIW) -- RFIW'17, based on the 1st ever large-scale visual kinship recognition data challenge workshop. RFIW'17 was organized with Families In the Wild (FIW)-the largest image database for kinship recognition. Kinship recognition has an abundance of practical uses. Including, but not limited to, forensic investigations, photo library management, historic lineage & genealogical studies, social-media- based analysis, cases of missing children & human tracking, and problems of immigration & border patrol. This workshop will cover the results of the data challenge, the current status and plans for FIW, and the past, present and future of kinship recognition. RFIW gives researchers and practitioners a unique opportunity to exchange their perspectives with others interested in the various aspects of kinship recognition technologies. \u0000 \u0000Participants of the competition spanned all over the globe. We had nearly 100 teams register for the event, with 12 teams submitting competitive results, and 6 of which had their work published. \u0000 \u0000RFIW'17 workshop is on 27 October 2017 at ACM MM. The agenda includes the following: \u0000RFIW Data Workshop Introduction and Overview \u0000Oral presentations (2) given by top performers of RFIW \u0000Poster Presentations by all other authors during coffee break \u0000Past, Present, and Future of Kinship Recognition, FIW, and upcoming events \u0000 \u0000 \u0000 \u0000Additionally, we have two great keynotes coming to share complimentary views of the problem: \u0000Rapid DNA Performance Results on Family Relationship Verification, Christopher Miles (Department of Homeland Security) \u0000Recent Advances in Deep Reinforcement Learning for Computer Vision and NLP, Caiming Xiong (Salesforce Research) \u0000 \u0000 \u0000 \u0000We encourage attendees to join us to learn from these valuable and insightful speakers that will guide us to a better understanding of the future.","PeriodicalId":209776,"journal":{"name":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kin-Verification Model on FIW Dataset Using Multi-Set Learning and Local Features 基于多集学习和局部特征的FIW数据集亲属验证模型

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3134423

Eran Dahan, Y. Keller, Shahar Mahpod

引用次数: 12

Session details: Keynote & Invited Talks 会议详情:主题演讲和特邀演讲

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3247930

Joseph P. Robinson

引用次数: 0

Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP 面向计算机视觉和自然语言处理的深度强化学习研究进展

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3137039

Caiming Xiong

引用次数: 3

KinNet

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3134425

Yong Li, Jiabei Zeng, Jie Zhang, Anbo Dai, Meina Kan, S. Shan, Xilin Chen

{"title":"KinNet","authors":"Yong Li, Jiabei Zeng, Jie Zhang, Anbo Dai, Meina Kan, S. Shan, Xilin Chen","doi":"10.1145/3134421.3134425","DOIUrl":"https://doi.org/10.1145/3134421.3134425","url":null,"abstract":"Automatic kinship verification has attracted increasing attentions as it holds promise to an abundance of applications. However, existing kinship verification methods suffer from the lack of large scale real-world data. Without enough training data, it is difficult to learn proper features that are discriminant for blood-related peoples. In this work, we propose KinNet, a fine-to-coarse deep metric learning framework for kinship verification. In the framework, we transfer knowledge from the large-scale-data-driven face recognition task, which is a fine-grained version of kinship recognition, by pre-training the network with massive data for face recognition. Then, the network is fine-tuned to find a metric space where kin-related peoples are discriminant. The metric space is learned by minimizing a soft triplet loss on the augmented kinship dataset. An augmented strategy is proposed to balance the amount of images per family member. Finally, we ensemble four networks to further boost the performance. The experimental results on the 1st Large-Scale Kinship Recognition Data Challenge (Track 1) demonstrate that our KinNet achieves the state-of-the-art performance in kinship verification.","PeriodicalId":209776,"journal":{"name":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 36

AdvNet: Adversarial Contrastive Residual Net for 1 Million Kinship Recognition 百万亲属识别的对抗对比剩余网

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3134422

Qingyan Duan, Lei Zhang

{"title":"AdvNet: Adversarial Contrastive Residual Net for 1 Million Kinship Recognition","authors":"Qingyan Duan, Lei Zhang","doi":"10.1145/3134421.3134422","DOIUrl":"https://doi.org/10.1145/3134421.3134422","url":null,"abstract":"Kinship verification in the wild is an interesting and challenging problem. The goal of kinship verification is to determine whether a pair of faces are blood relatives or not. Most previous methods for kinship verification can be divided as hand-crafted features based shallow learning methods and convolutional neural network (CNN) based deep learning methods. Nevertheless, these methods are still posed with the challenging task of recognizing kinship cues from facial images. Part of the reason for this may be that, the family information and the distribution difference of pairwise kin-face data based kinship verification issue are rarely considered. Inspired by maximum mean discrepancy (MMD) and generative adversarial net (GAN), family ID based Adversarial contrastive residual Network (AdvNet) is proposed for large-scale (1 Million) kinship recognition in this paper. The MMD based adversarial loss (AL), pairwise contrastive loss (CL) and family ID based softmax loss (SL) are jointly formulated in the proposed AdvNet for kin-relation enhancement and discovery. Further, the deep nets ensemble is used for deep kin-feature augmentation. Finally, Euclidean distance metric is used for kinship recognition. Extensive experiments on the 1st Large-Scale Kinship Recognition Data Challenge (Families in the wild) show the effectiveness of our proposed AdvNet and ensemble based feature augmentation.","PeriodicalId":209776,"journal":{"name":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127489078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

RFIW 2017: LPQ-SIEDA for Large Scale Kinship Verification RFIW 2017: LPQ-SIEDA大规模亲属关系验证

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3134426

Oualid Laiadi, A. Ouamane, A. Benakcha, A. Taleb-Ahmed

引用次数: 7

Rapid DNA Performance Results on Family Relationship Verification 家庭关系验证的快速DNA性能结果

Proceedings of the 2017 Workshop on Recognizing Families In the Wild Pub Date : 2017-10-27 DOI: 10.1145/3134421.3137040

Christopher Miles

{"title":"Rapid DNA Performance Results on Family Relationship Verification","authors":"Christopher Miles","doi":"10.1145/3134421.3137040","DOIUrl":"https://doi.org/10.1145/3134421.3137040","url":null,"abstract":"The Department of Homeland Security (DHS) Science and Technology Directorate (S&T), working with other Federal partners, developed Rapid DNA technology to meet a DHS requirement to verify family relationship claims to reduce fraud and expedite legal immigration. Several hundred thousand immigration relationship tests are processed annually for DHS by AABB accredited laboratories. The AABB Relationship Testing Subcommittee sets the standards for those tests and accreditation of relationship testing laboratories. But, shipping of the collection kits overseas and coordinating DNA collection can cause these tests to take weeks to months to process and results to be returned. Rapid DNA uses microfluidics technology to reduce the million-dollar clean-room DNA processes down to a standalone, integrated, and automated desktop system that processes five to seven DNA samples in 90 minutes in a closed, hands-off system. Rapid DNA is built to be operated by DHS field officers and agents. It is about the size of a laser printer, and can be operated in an office setting by anyone who can change a printer cartridge. It is also ruggedized to military requirements, and can be operated in hazardous field environments on generator power. Setup takes just 15 minutes. With success showing that Rapid DNA accurately verifies family relationships, additional DHS needs were identified to fight human trafficking along U.S. borders and reunify families following critical or mass-casualty incidents. This presentation will discuss the DHS needs for family relationship verification, AABB Standards, and Rapid DNA performance and field test results.","PeriodicalId":209776,"journal":{"name":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129486421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0