2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)最新文献

Calorific Expenditure Estimation Using Deep Convolutional Network Features 基于深度卷积网络特征的热量消耗估算

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-04-26 DOI: 10.1109/WACVW.2018.00014

Baodong Wang, L. Tao, T. Burghardt, M. Mirmehdi

引用次数: 4

Learning Visual Engagement for Trauma Recovery 学习视觉参与创伤恢复

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00016

Svati Dhamija, T. Boult

引用次数: 0

Facial Expression Recognition Using a Large Out-of-Context Dataset 基于大型非上下文数据集的面部表情识别

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00012

Elizabeth Tran, Michael B. Mayhew, Hyojin Kim, P. Karande, A. Kaplan

引用次数: 6

Evaluating a Convolutional Neural Network on Short-Wave Infra-Red Images 短波红外图像的卷积神经网络评价

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00008

M. Bihn, Manuel Günther, Daniel Lemmond, T. Boult

{"title":"Evaluating a Convolutional Neural Network on Short-Wave Infra-Red Images","authors":"M. Bihn, Manuel Günther, Daniel Lemmond, T. Boult","doi":"10.1109/WACVW.2018.00008","DOIUrl":"https://doi.org/10.1109/WACVW.2018.00008","url":null,"abstract":"Machine learning algorithms, both traditional and neuralnetwork-based, have been tested against RGB facial images for years, but these algorithms are prone to fail when illumination conditions are insufficient, for example, at night or when images are taken from long distances. Short-Wave Infra-Red (SWIR) illumination provides a much higher intensity and a much more ambient structure than visible light, which makes it better suited for face recognition in different conditions. However, current neural networks require lots of training data, which is not available in the SWIR domain. In this paper, we examine the ability of a convolutional neural network, specifically, the VGG Face network, which was trained on visible spectrum images, to work on SWIR images. Utilizing a dataset containing both RGB and SWIR images, we hypothesize that the VGG Face network will perform well both on facial images taken in RGB and SWIR wavelengths. We expect that the features extracted with VGG Face are independent of the actual wavelengths that the images were taken with. Thus, face recognition with VGG Face is possible between the RGB and SWIR domains. We find that VGG Face performs reasonable on some of the SWIR wavelengths. We can almost reach the same recognition performance when using composite images built from three SWIR wavelengths probing on RGB.","PeriodicalId":301220,"journal":{"name":"2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803902","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: 1

Facial Attributes Guided Deep Sketch-to-Photo Synthesis 面部属性引导深度素描到照片合成

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00006

Hadi Kazemi, S. M. Iranmanesh, Ali Dabouei, Sobhan Soleymani, N. Nasrabadi

{"title":"Facial Attributes Guided Deep Sketch-to-Photo Synthesis","authors":"Hadi Kazemi, S. M. Iranmanesh, Ali Dabouei, Sobhan Soleymani, N. Nasrabadi","doi":"10.1109/WACVW.2018.00006","DOIUrl":"https://doi.org/10.1109/WACVW.2018.00006","url":null,"abstract":"Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry. Despite the significant improvements in sketch-to-photo synthesis techniques, existing methods have still serious limitations in practice, such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image. In this work, we present a new framework, which is a conditional version of Cycle-GAN, conditioned on facial attributes. The proposed network forces facial attributes, such as skin and hair color, on the synthesized photo and does not need a set of aligned face-sketch pairs during its training. We evaluate the proposed network by training on two real and synthetic sketch datasets. The hand-sketch images of the FERET dataset and the color face images from the WVU Multi-modal dataset are used as an unpaired input to the proposed conditional CycleGAN with the skin color as the controlled face attribute. For more attribute guided evaluation, a synthetic sketch dataset is created from the CelebA dataset and used to evaluate the performance of the network by forcing several desired facial attributes on the synthesized faces.","PeriodicalId":301220,"journal":{"name":"2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887802","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: 30

A Multi-task Convolutional Neural Network for Joint Iris Detection and Presentation Attack Detection 一种多任务卷积神经网络联合虹膜检测与表示攻击检测

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00011

Cunjian Chen, A. Ross

引用次数: 64

Generic Object Discrimination for Mobile Assistive Robots Using Projective Light Diffusion 基于投影光扩散的移动辅助机器人一般目标识别

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-15 DOI: 10.1109/WACVW.2018.00013

P. Papadakis, David Filliat

引用次数: 3

ivisX: An Integrated Video Investigation Suite for Forensic Applications ivisX:集成视频调查套件的法医应用

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-01 DOI: 10.1109/WACVW.2018.00007

Chengchao Qu, J. Metzler, Eduardo Monari

{"title":"ivisX: An Integrated Video Investigation Suite for Forensic Applications","authors":"Chengchao Qu, J. Metzler, Eduardo Monari","doi":"10.1109/WACVW.2018.00007","DOIUrl":"https://doi.org/10.1109/WACVW.2018.00007","url":null,"abstract":"Video data from surveillance cameras are nowadays an important instrument for investigating crimes and identifying the identity of an offender. The analysis of the mass data acquired from numerous cameras poses enormous challenges to police investigation authorities. Supporting softwares and video management tools currently on the market focus either on elaborate visualization and editing of video data, specific image processing or video content analysis tasks. As a result, such a scattered system landscape further exacerbates the complexity and difficulty of a timely analysis of the available data. This work presents our unified framework ivisX, which is an integrated suite to simplify the entire workflow of video data investigation. The algorithmic backbone of ivisX is built upon an effective content-based search algorithm using region covariance for low-resolution (LR) data and a novel 3D face super-resolution (FSR) approach, which can generate high-resolution (HR) 3D face models to render high-quality facial composites with a single blurred and pixelated face image of the LR domain. Moreover, ivisX has a modular design, which allows for flexible incorporation of various extensions ranging from processing and display of video data from multiple cameras to analysis and documentation of the results into a powerful integrated toolkit to assist forensic investigation.","PeriodicalId":301220,"journal":{"name":"2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125853802","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: 4

Enhancing Optical Cross-Sensor Fingerprint Matching Using Local Textural Features 利用局部纹理特征增强光学指纹匹配

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-01 DOI: 10.1109/WACVW.2018.00010

Emanuela Marasco, Alex Feldman, Keleigh Rachel Romine

引用次数: 5

Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context 非合作环境下基于面部和手部生物特征的自动访问控制

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) Pub Date : 2018-03-01 DOI: 10.1109/WACVW.2018.00009

M. N. Jahromi, Morten Bojesen Bonderup, Maryam Asadi-Aghbolaghi, Egils Avots, Kamal Nasrollahi, Sergio Escalera, S. Kasaei, T. Moeslund, G. Anbarjafari

{"title":"Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context","authors":"M. N. Jahromi, Morten Bojesen Bonderup, Maryam Asadi-Aghbolaghi, Egils Avots, Kamal Nasrollahi, Sergio Escalera, S. Kasaei, T. Moeslund, G. Anbarjafari","doi":"10.1109/WACVW.2018.00009","DOIUrl":"https://doi.org/10.1109/WACVW.2018.00009","url":null,"abstract":"Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.","PeriodicalId":301220,"journal":{"name":"2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115912669","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: 6