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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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QuadricSLAM: Dual Quadrics as SLAM Landmarks 31

QuadricSLAM: Dual Quadrics as SLAM Landmarks

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

作者： Nicholson, Lachlan Milford, Michael Sunderhauf, Niko Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld Australia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D bounding boxes (such as those typically obtained from visual object detection systems) can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for deep-learned object detectors that addresses the challenge of partial object detections often encountered in robotics applications, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.

关键词： Simultaneous localization and mapping Cameras Object detection Trajectory Robot vision systems Three-dimensional displays

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

引用

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

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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Learning Network Architectures of Deep CNNs under Resource Constraints 31

Learning Network Architectures of Deep CNNs under Resource C...

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

作者： Chan, Michael Scarafoni, Daniel Duarte, Ronald Thornton, Jason Skelly, Luke MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyper-parameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully automated model learning approach that (1) treats architecture selection as part of the learning process, (2) uses a blend of broad-based random sampling and adaptive iterative refinement to explore the solution space, (3) performs optimization subject to given memory and computational constraints imposed by target deployment scenarios, and (4) is scalable and can use only a practically small number of GPUs for training. We present results that show graceful model degradation under strict resource constraints for object classification problems using CIFAR-10 in our experiments. We also discuss future work in further extending the approach.

关键词： computer architecture Computational modeling Optimization Adaptation models Network architecture Linear programming Training

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Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits 31

Incorporating Touch Biometrics to Mobile One-Time Passwords:...

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

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab E-28049 Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work evaluates the advantages and potential of incorporating touch biometrics to mobile one-time passwords (OTP). The new e-BioDigit database, which comprises on-line handwritten numerical digits from 0 to 9, has been acquired using the finger touch as input to a mobile device. This database is used in the experiments reported in this work and it is publicly available to the research community. An analysis of the OTP scenario using handwritten digits is carried out regarding which are the most discriminative handwritten digits and how robust the system is when increasing the number of them in the user password. Additionally, the best features for each handwritten numerical digit are studied in order to enhance our proposed biometric system. Our proposed approach achieves remarkable results with EERs ca. 5.0% when using skilled forgeries, outperforming other traditional biometric verification traits such as the handwritten signature or graphical passwords on similar mobile scenarios.

关键词： Biometrics (access control) Databases Password Mobile handsets Feature extraction Protocols

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Priming Neural Networks 31

Priming Neural Networks

引用

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

作者： Rosenfeld, Amir Biparva, Mandi Tsotsos, John K. York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise, small size or atypical appearance. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.

关键词： Visualization Task analysis Object detection Neural networks Semantics computer vision Image segmentation

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ComboGAN: Unrestrained Scalability for Image Domain Translation 31

ComboGAN: Unrestrained Scalability for Image Domain Translat...

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

作者： Anoosheh, Asha Agustsson, Eirikur Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol D ITET Comp Vis Lab Zurich Switzerland Swiss Fed Inst Technol D ITET Zurich Switzerland Merantix Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need ( 14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs;whereas our model requires only 14 generator/discriminator pairs.

关键词： Training Generators Gallium nitride Decoding computer vision Task analysis Data models

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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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Image Caption Generation with Hierarchical Contextual Visual Spatial Attention 31

Image Caption Generation with Hierarchical Contextual Visual...

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

作者： Khademi, Mahmoud Schulte, Oliver Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on two-dimensional context, by selecting or ignoring its input. The Grid LSTM has not been applied to image caption generation task before. Another novel aspect is that we leverage a set of local region-grounded texts obtained by transfer learning. The region-grounded texts often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word, while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on MS-COCO dataset show that our architecture outperforms the state-of-the-art.

关键词： Feature extraction Visualization Logic gates computer architecture Task analysis Context modeling Computational modeling

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Automatic Cricket Highlight generation using Event-Driven and Excitement-Based features 31

Automatic Cricket Highlight generation using Event-Driven an...

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

作者： Shukla, Pushkar Sadana, Hemant Bansal, Apaar Verma, Deepak Elmadjian, Carlos Raman, Balasubramanian Turk, Matthew Univ Calif Santa Barbara Santa Barbara CA 93106 USA Indian Inst Technol Roorkee Uttar Pradesh India Univ Sao Paulo Sao Paulo Brazil Raman Classes Roorkee Uttar Pradesh India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Producing sports highlights is a labor-intensive work that requires some degree of specialization. We propose a model capable of automatically generating sports highlights with a focus on cricket. Cricket is a sport with a complex set of rules and is played for a longer time than most other sports. In this paper we propose a model that considers both event-based and excitement-based features to recognize and clip important events in a cricket match. Replays, audio intensity, player celebration, and playfield scenarios are examples of cues used to capture such events. To evaluate our framework, we conducted a set of experiments ranging from user studies to a comparison analysis between our highlights and the ones provided by the official broadcasters. The general approval by users and the significant overlap between both kinds of highlights indicate the usefulness of our model in real-life scenarios.

关键词： Sports Games Hidden Markov models Support vector machines Optical character recognition software Cameras Training

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