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Covariance Pooling for Facial Expression recognition 31

Covariance Pooling for Facial Expression Recognition

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Acharya, Dinesh Huang, Zhiwu Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven VISICS Leuven Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial features. In this work, we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition. In particular, we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end-to-end deep learning manner. By doing so, we are able to achieve a recognition accuracy of 58.14% on the validation set of Static Facial Expressions in the Wild (SFEW 2.0) and 87.0% on the validation set of Real-World Affective Faces (RAF) Database(1). Both of these results are the best results we are aware of. Besides, we leverage covariance pooling to capture the temporal evolution of per-frame features for video-based facial expression recognition. Our reported results demonstrate the advantage of pooling image-set features temporally by stacking the designed manifold network of covariance pooling on top of convolutional network layers.

关键词： Face recognition Covariance matrices Manifolds Image recognition Videos Feature extraction Standards

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Deep Features for Recognizing Disguised Faces in the Wild 31

Deep Features for Recognizing Disguised Faces in the Wild

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bansal, Ankan Ranjan, Rajeev Castillo, Carlos D. Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538661000

Unconstrained face verification is a challenging problem owing to variations in pose, illumination, resolution of image, age, etc. This problem becomes even more complex when the subjects are actively trying to deceive face verification systems by wearing a disguise. The problem under consideration here is to identify a subject under disguises and reject impostors trying to look like the subject of interest. In this paper we present a DCNN-based approach for recognizing people under disguises and picking out impostors. We train two different networks on a large dataset comprising of still images and video frames with L2-softmax loss. We fuse features obtained from the two networks and show that the resulting features are effective for discriminating between disguised faces and impostors in the wild. We present results on the recently introduced Disguised Faces in the Wild challenge dataset.

关键词： Training Face recognition Pipelines Measurement Three-dimensional displays Face detection Feature extraction

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Identity Aware Synthesis for Cross Resolution Face recognition 31

Identity Aware Synthesis for Cross Resolution Face Recogniti...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Singh, Maneet Nagpal, Shruti Vatsa, Mayank Singh, Richa Majumdar, Angshul IIIT Delhi New Delhi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Enhancing low resolution images via super-resolution or synthesis algorithms for cross-resolution face recognition has been well studied. Several image processing and machine learning paradigms have been explored for addressing the same. In this research, we propose Synthesis via Hierarchical Sparse Representation (SHSR) algorithm for synthesizing a high resolution face image from a low resolution input image. The proposed algorithm learns multi-level sparse representation for both high and low resolution gallery images, along with identity aware dictionaries and a transformation function between the two representations for face identification scenarios. With low resolution test data as input, a high resolution test image is synthesized using the identity aware dictionaries and transformation, which is then used for face recognition. The performance of the proposed SHSR algorithm is evaluated on four datasets, including one real world dataset. Experimental results and comparison with seven existing algorithms demonstrate the efficacy of the proposed algorithm in terms of both face identification and image quality measures.

关键词： Image resolution Dictionaries Face Face recognition Image recognition Training Machine learning

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Pose Encoding for Robust Skeleton-Based Action recognition 31

Pose Encoding for Robust Skeleton-Based Action Recognition

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Demisse, Girum G. Papadopoulos, Konstantinos Aouada, Djamila Ottersten, Bjorn Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust 29 Ave JF Kennedy L-1855 Luxembourg Luxembourg

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations. Earlier works in skeleton-based action recognition explored different approaches for filtering linear noise transformations, but neglect to address potential nonlinear transformations. In this paper, we present an unsupervised learning approach for estimating nonlinear noise transformations in pose estimates. Our approach starts by decoupling linear and nonlinear noise transformations. While the linear transformations are modelled explicitly the nonlinear transformations are learned from data. Subsequently, we use an autoencoder with L2-norm reconstruction error and show that it indeed does capture nonlinear noise transformations, and recover a denoised pose estimate which in turn improves performance significantly. We validate our approach on a publicly available dataset, NW-UCLA.

关键词： Noise measurement Data models Decoding Cameras Noise reduction Training Unsupervised learning

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DIY Human Action dataset Generation 31

DIY Human Action dataset Generation

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khodabandeh, Mehran Joze, Hamid Reza Vaezi Zharkov, Ilya Pradeep, Vivek Simon Fraser Univ Burnaby BC Canada Microsoft Redmond WA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing dataset, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition datasets, that this new data generation technique can improve the performance of current action recognition neural nets.

关键词： Skeleton Gallium nitride Task analysis Trajectory Generators computer vision Training

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Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo recognition 31

Attribute-Centered Loss for Soft-Biometrics Guided Face Sket...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kazemi, Hadi Soleymani, Sobhan Dabouei, Ali Iranmanesh, Mehdi Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Face sketches are able to capture the spatial topology of a face while lacking some facial attributes such as race, skin, or hair color. Existing sketch-photo recognition approaches have mostly ignored the importance of facial attributes. In this paper, we propose a new loss function, called attribute-centered loss, to train a Deep Coupled Convolutional Neural Network (DCCNN) for facial attribute guided sketch to photo matching. Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes. The DCCNN simultaneously is trained to map photos and pairs of testified attributes and corresponding forensic sketches around their associated centers, while preserving the spatial topology information. Importantly, the centers learn to keep a relative distance from each other, related to their number of contradictory attributes. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D Semi-forensic) databases. The proposed method significantly outperforms the state-of-the-art.

关键词： Facial features Face Face recognition Feature extraction Neural networks Skin Task analysis

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Fusion of Handcrafted and Deep Learning Features for Large-scale Multiple Iris Presentation Attack Detection 31

Fusion of Handcrafted and Deep Learning Features for Large-s...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yadav, Daksha Kohli, Naman Agarwal, Akshay Vatsa, Mayank Singh, Richa Noore, Afzel West Virginia Univ Morgantown WV 26506 USA IIIT Delhi New Delhi India Texas A&M Univ Kingsville Kingsville TX USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Iris recognition systems may be vulnerable to presentation attacks such as textured contact lenses, print attacks, and synthetic iris images. Increasing applications of iris recognition have raised the importance of efficient presentation attack detection algorithms. In this paper, we propose a novel algorithm for detecting iris presentation attacks using a combination of handcrafted and deep learning based features. The proposed algorithm combines local and global Haralick texture features in multi-level Redundant Discrete Wavelet Transform domain with VGG features to encode the textural variations between real and attacked iris images. The proposed algorithm is extensively tested on a large iris dataset comprising more than 270,000 real and attacked iris images and yields a total error of 1.01%. The experimental evaluation demonstrates the superior presentation attack detection performance of the proposed algorithm as compared to state-of-the-art algorithms.

关键词： Iris recognition Iris Databases Lenses Feature extraction Image segmentation

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Detection of Distracted Driver using Convolutional Neural Network 31

Detection of Distracted Driver using Convolutional Neural Ne...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Baheti, Bhakti Gajre, Suhas Talbar, Sanjay SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Number of road accidents is continuously increasing in last few years worldwide. As per the survey of National Highway Traffic Safety Administrator, nearly one in five motor vehicle crashes are caused by distracted driver. We attempt to develop an accurate and robust system for detecting distracted driver and warn him against it. Motivated by the performance of Convolutional Neural Networks in computer vision, we present a CNN based system that not only detects the distracted driver but also identifies the cause of distraction. VGG-16 architecture is modified for this particular task and various regularization techniques are implied in order to improve the performance. Experimental results show that our system outperforms earlier methods in literature achieving an accuracy of 96.31% and processes 42 images per second on GPU. We also study the effect of dropout, L2 regularization and batch normalisation on the performance of the system. Next, we present a modified version of our architecture that achieves 95.54% classification accuracy with the number of parameters reduced from 140M in original VGG-16 to 15M only.

关键词： Vehicles computer architecture Vehicle crash testing Convolutional neural networks Task analysis Wheels Roads

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Convolutional Neural Networks for Iris Presentation Attack Detection: Toward Cross-Dataset and Cross-Sensor Generalization 31

Convolutional Neural Networks for Iris Presentation Attack D...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hoffman, Steven Sharma, Renu Ross, Arun Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Iris recognition systems are vulnerable to presentation attacks where an adversary employs artifacts such as 2D prints of the eye, plastic eyes, and cosmetic contact lenses to obfuscate their own identity or to spoof the identity of another subject. In this work, we design a Convolutional Neural Network (CNN) architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability. The salient features of the proposed approach include: (a) the use of the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss;(b) the tessellation of the iris region into overlapping patches to enable data augmentation as well as to learn features that are location agnostic;(c) fusion of information across patches to enhance detection accuracy;(d) incorporating a "segmentation mask" in order to automatically learn the relative importance of the pupil and iris regions;(e) generation of a "heat map" that displays patch-wise presentation attack information, thereby accounting for artifacts that may impact only a small portion of the iris region. Experiments confirm the efficacy of the proposed approach.

关键词： Iris recognition Iris Image segmentation Plastics Sensors Lenses Hardware

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Onboard Stereo vision for Drone Pursuit or Sense and Avoid 31

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cigla, Cevahir Thakker, Rohan Matthies, Larry Aselsan Inc Yenimahalle Ankara Turkey CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We describe a new, on-board, short range perception system that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

关键词： Radar tracking Target tracking Drones Cameras Three-dimensional displays Observers

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