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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 90 条记录，以下是71-80 订阅

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Automatic recognition of Emotions and Membership in Group Videos 29

Automatic Recognition of Emotions and Membership in Group Vi...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mou, Wenxuan Gunes, Hatice Patras, Ioannis Queen Mary Univ London London England Univ Cambridge Cambridge England

ISBN: (纸本)9781509014378

Automatic affect analysis and understanding has become a well established research area in the last two decades. However, little attention has been paid to the analysis of the affect expressed in group settings, either in the form of affect expressed by the whole group collectively or affect expressed by each individual member of the group. this paper presents a framework which, in group settings automatically classifies the affect expressed by each individual group member along both arousal and valence dimensions. We first introduce a novel Volume Quantised Local Zernike Moments Fisher Vectors (vQLZM-FV) descriptor to represent the facial behaviours of individuals in the spatio-temporal domain and then propose a method to recognize the group membership of each individual (i.e., which group the individual in question is part of) by using their face and body behavioural cues. We conduct a set of experiments on a newly collected dataset that contains fourteen recordings of four groups, each consisting of four people watching affective movie stimuli. Our experimental results show that (1) the proposed vQLZM-FV outperforms the other feature representations in affect recognition, and (2) group membership can be recognized using the non-verbal face and body features, indicating that individuals influence each other's behaviours within a group setting.

关键词： Emotion recognition

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A Comprehensive Analysis of Deep Learning Based Representation for Face recognition 29

A Comprehensive Analysis of Deep Learning Based Representati...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ghazi, Mostafa Mehdipour Ekenel, Hazim Kemal Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Istanbul Tech Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. these approaches have been extensively tested on such unconstrained datasets, on the Labeled Faces in the Wild and YouTube Faces, to name a few. However, their capability to handle individual appearance variations caused by factors such as head pose, illumination, occlusion, and misalignment has not been thoroughly assessed till now. In this paper, we present a comprehensive study to evaluate the performance of deep learning based face representation under several conditions including the varying head pose angles, upper and lower face occlusion, changing illumination of different strengths, and misalignment due to erroneous facial feature localization. Two successful and publicly available deep learning models, namely VGG-Face and Lightened CNN have been utilized to extract face representations. the obtained results show that although deep learning provides a powerful representation for face recognition, it can still benefit from preprocessing, for example, for pose and illumination normalization in order to achieve better performance under various conditions. Particularly, if these variations are not included in the dataset used to train the deep learning model, the role of preprocessing becomes more crucial. Experimental results also show that deep learning based representation is robust to misalignment and can tolerate facial feature localization errors up to 10% of the interocular distance.

关键词： Face recognition

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Covert Video Classification by Codebook Growing pattern 29

Covert Video Classification by Codebook Growing Pattern

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Du, Liang Lang, Haitao Tian, Ying-Li Tan, Chiu C. Wu, Jie Ling, Haibin Temple Univ Philadelphia PA 19122 USA Beijing Univ Chem Technol Beijing Peoples R China City Univ New York New York NY USA

ISBN: (纸本)9781509014378

Recent advances in visual data acquisition and Internet technologies make it convenient and popular to collect and share videos. these activities, however, also raise the issue of privacy invasion. One potential privacy threat is the unauthorized capture and/or sharing of covert videos, which are recorded without the awareness of the subject(s) in the video. Automatic classification of such videos can provide an important basis toward addressing relevant privacy issues. the task is very challenging due to the large intra-class variation and between-class similarity, since there is no limit in the content of a covert video and it may share very similar content with a regular video. the challenge brings troubles when applying existing content-based video analysis methods to covert video classification. In this paper, we propose a novel descriptor, codebook growing pattern (CGP), which is derived from latent Dirichlet allocation (LDA) over optical flows. Given an input video V, we first represent it with a sequence of histograms of optical flow (HOF). After that, these HOFs are fed into LDA to dynamically generate the codebook for V. the CGP descriptor is then defined as the growing codebook sizes in the LDA procedure. CGP fits naturally for covert video representation since (1) optical flows can capture the camera motion that characterizes the covert video acquisition, and (2) CGP by itself is insensitive to video content. To evaluate the proposed approach, we collected a large covert video dataset, the first such dataset to our knowledge, and tested the proposed method on the dataset. the results show clearly the effectiveness of the proposed approach in comparison with other state-of-the-art video classification algorithms.

关键词： Optical flows

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A Combined EM and Visual Tracking Probabilistic Model for Robust Mosaicking: Application to Fetoscopy 29

A Combined EM and Visual Tracking Probabilistic Model for Ro...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Tella, Marcel Daga, Pankaj Chadebecq, Francois thompson, Stephen Shakir, Dzhoshkun I. Dwyer, George Wimalasundera, Ruwan Deprest, Jan Stoyanov, Danail Vercauteren, Tom Ourselin, Sebastien UCL Translat Imaging Grp CMIC London WC1E 6BT England UCL Surg Robot Vis Grp CMIC London WC1E 6BT England Univ Hosp Leuven Dept Obstet & Gynaecol Leuven Belgium Univ Coll Hosp London England

ISBN: (纸本)9781509014378

Twin-to-Twin Transfusion Syndrome (TTTS) is a progressive pregnancy complication in which inter-twin vascular connections in the shared placenta result in a blood flow imbalance between the twins. the most effective therapy is to sever these connections by laser photo-coagulation. However, the limited field of view of the fetoscope hinders their identification. A potential solution is to augment the surgeon's view by creating a mosaic image of the placenta. State-of-the-art mosaicking methods use feature-based approaches, which have three main limitations: (i) they are not robust against corrupt data e.g. blurred frames, (ii) temporal information is not used, (iii) the resulting mosaic suffers from drift. We introduce a probabilistic temporal model that incorporates electromagnetic and visual tracking data to achieve a robust mosaic with reduced drift. By assuming planarity of the imaged object, the nRT decomposition can be used to parametrize the state vector. Finally, we tackle the non-linear nature of the problem in a numerically stable manner by using the Square Root Unscented Kalman Filter. We show an improvement in performance in terms of robustness as well as a reduction of the drift in comparison to state-of-the-art methods in synthetic, phantom and ex vivo datasets.

关键词： computer vision

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What do you do when you know that you don't know? 29

What do you do when you know that you don't know?

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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)

作者： Bendale, Abhijit Boult, Terrance E. Univ Colorado Colorado Springs Colorado Springs CO 80918 USA

ISBN: (纸本)9781509014378

Real-world biometrics recognition problems often have two unknowns: the person be recognized, as well as a hidden unknown - missing data. If we choose to ignore data that is occasionally missing, we sacrifice accuracy. In this paper, we present a novel technique to address the problem of handling missing data in biometrics systems without having to make implicit assumptions on the distribution of the underlying data. We introduce the concept of "operational adaptation" for biometric systems and formalize the problem. We present a solution for handling missing data based on refactoring on Support Vector Machines for large scale face recognition tasks. We also develop a general approach to estimating SVM refactoring risk. We present experiments on large-scale face recognition based on describable visual attributes on LFW dataset. Our approach consistently outperforms state-of-the-art methods designed to handle missing data.

关键词： Face recognition

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PETS 2016: Dataset and Challenge 29

PETS 2016: Dataset and Challenge

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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)

作者： Patino, Luis Cane, Tom Vallee, Alain Ferryman, James Univ Reading Computat Vis Grp Reading RG6 6AY Berks England BMT Grp Ltd Teddington TW11 8LZ Middx England SAGEM F-92659 Boulogne France

ISBN: (纸本)9781509014378

this paper describes the datasets and computer vision challenges that form part of the PETS 2016 workshop. PETS 2016 addresses the application of on-board multi sensor surveillance for protection of mobile critical assets. the sensors (visible and thermal cameras) are mounted on the asset itself and surveillance is performed around the asset. Two datasets are provided: (1) a multi sensor dataset as used for the PETS2014 challenge which addresses protection of trucks (the ARENA Dataset);and (2) a new dataset - the IPATCH Dataset - addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. the dataset specifically addresses several vision challenges set in the PETS 2016 workshop, and corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis ('simple' event detection: the behaviour recognition of a single actor) and High-Level Video Analysis ('complex' event detection: the behaviour and interaction recognition of several actors).

关键词： Monitoring

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A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets 29

A Comparison of Human and Automated Face Verification Accura...

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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)

作者： Blanton, Austin Allen, Kristen C. Miller, Tim Kalka, Nathan D. Jain, Anil K. Noblis Reston VA 20191 USA Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781509014378

Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.

关键词： Face recognition

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CALIPER: Continuous Authentication Layered with Integrated PKI Encoding recognition 29

CALIPER: Continuous Authentication Layered with Integrated P...

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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)

作者： Rudd, Ethan M. Boult, Terrance E. Univ Colorado Vis & Secur Technol VAST Lab Colorado Springs CO 80907 USA

ISBN: (纸本)9781509014378

Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the response to the layer which manages service/application access is not fake. In this paper, we introduce the CALIPER protocol, in which a separate Continuous Access Verification Entity (CAVE) directly challenges the user's identity in a continuous authentication regime. Instead of simply returning authentication probabilities or confidence scores, CALIPER's CAS uses live hard and soft biometric samples from the user to extract a cryptographic private key embedded in a challenge posed by the CAVE. the CAS then uses this key to sign a response to the CAVE. CALIPER supports multiple modalities, key lengths, and security levels and can be applied in two scenarios: One where the CAS must authenticate its user to a CAVE running on a remote server (device-server) for access to remote application data, and another where the CAS must authenticate its user to a locally running trusted computing module (TCM) for access to local application data (device-TCM). We further demonstrate that CALIPER can leverage device hardware resources to enable privacy and security even when the device's kernel is compromised, and we show how this authentication protocol can even be expanded to obfuscate direct kernel object manipulation (DKOM) malwares.

关键词： Authentication

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Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit recognition and Facial Landmark Detection 29

Constrained Joint Cascade Regression Framework for Simultane...

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2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Wu, Yue Ji, Qiang Rensselaer Polytech Inst ECSE Dept 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781467388511

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. the experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.

关键词： Iterative methods

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Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled 29

Deep Hand: How to Train a CNN on 1 Million Hand Images When ...

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2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Koller, Oscar Ney, Hermann Bowden, Richard Rhein Westfal TH Aachen Human Language Technol & Pattern Recog Aachen Germany Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781467388511

this work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. this allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. the iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. the algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

关键词： Iterative methods

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