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Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN 31

Hybrid User-Independent and User-Dependent Offline Signature...

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

作者： Yilmaz, Mustafa Berkay Ozturk, Kagan Akdeniz Univ Antalya Turkey

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Signature verification task needs relevant signature representations to achieve low error rates. Many signature representations have been proposed so far. In this work we propose a hybrid user-independent/dependent offline signature verification technique with a two-channel convolutional neural network (CNN) both for verification and feature extraction. Signature pairs are input to the CNN as two channels of one image, where the first channel always represents a reference signature and the second channel represents a query signature. We decrease the image size through the network by keeping the convolution stride parameter large enough. Global average pooling is applied to decrease the dimensionality to 200 at the end of locally connected layers. We utilize the CNN as a feature extractor and report 4.13% equal error rate (EER) considering 12 reference signatures with the proposed 200-dimensional representation, compared to 3.66% of a recently proposed technique with 2048-dimensional representation using the same experimental protocol. When the two methods are combined at score level, more than 50% improvement (1.76% EER) is achieved demonstrating the complementarity of them. Sensitivity of the model to gray-level and binary images is investigated in detail. One model is trained using gray-level images and the other is trained using binary images. It is shown that the availability of gray-level information in train and test data decreases the EER e.g. from 11.86% to 4.13%.

关键词： Feature extraction Forgery Training Error analysis Databases Measurement computer architecture

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From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation 31

From apparent to real age: gender, age, ethnic, makeup, and ...

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

作者： Clapes, Albert Anbarjafari, Gholamreza Bilici, Ozan Temirova, Dariia Avots, Egils Escalera, Sergio Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain Univ Tartu iCV Lab Tartu Estonia GoSwift Inc Tartu Estonia Hasan Kalyoncu Univ Gaziantep Turkey

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Real age estimation in still images of faces is an active area of research in the computer vision community. However, very few works attempted to analyse the apparent age as perceived by observers. Apparent age estimation is a subjective task, which is affected by many factors present in the image as well as by observer's characteristics. In this work, we enhance the APPA-REAL dataset, containing around 8K images with real and apparent ages, with new annotated attributes, namely gender, ethnic, makeup, and expression. Age and gender from a subset of guessers is also provided. We show there exists some consistent bias for a subset of these attributes when relating apparent to real age. In addition we run simple experiments with a basic Convolutional Neural Network (CNN) showing that considering apparent labels for training improves real age estimation rather than training with real ages. We also perform bias correction on CNN predictions, showing that it further enhance final age recognition performance.

关键词： Estimation computer vision Databases Visualization Training Aging Support vector machines

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Deep-BCN: Deep networks meet biased competition to create a brain-inspired model of attention control 31

Deep-BCN: Deep networks meet biased competition to create a ...

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

作者： Adeli, Hossein Zelinsky, Gregory SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The mechanism of attention control is best described by biased-competition theory (BCT), which suggests that a top-down goal state biases a competition among object representations for the selective routing of a visual input for classification. Our work advances this theory by making it computationally explicit as a deep neural network (DNN) model, thereby enabling predictions of goal-directed attention control using real-world stimuli. This model, which we call Deep-BCN, is built on top of an 8-layer DNN pre-trained for object classification, but has layers mapped to early visual (V1, V2/V3, V4), ventral (PIT, AIT), and frontal (PFC) brain areas that have their functional connectivity informed by BCT. Deep-BCN also has a superior colliculus and a frontal-eye field, and can therefore make eye movements. We compared Deep-BCN's eye movements to those made from 15 people performing a categorical search for one of 25 target object categories, and found that it predicted both the number of fixations during search and the saccade-distance travelled before search termination. With Deep-BCN a DNN implementation of BCT now exists, which can be used to predict the neural and behavioral responses of an attention control mechanism as it mediates a goal-directed behavior-in our study the eye movements made in search of a target goal.

关键词： Visualization Brain modeling Computational modeling Object detection Search problems computer vision

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Towards dense object tracking in a 2D honeybee hive 31

Towards dense object tracking in a 2D honeybee hive

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bozek, Katarzyna Hebert, Laetitia Mikheyev, Alexander S. Stephens, Greg J. OIST Grad Univ Okinawa Japan Vrije Univ Amsterdam Fac Sci Amsterdam Netherlands

ISBN: (纸本)9781538664209

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of similar to 7% of a typical body dimension, and orientation error of 12, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.

关键词： Labeling Video recording Object tracking Image recognition Image resolution Light emitting diodes

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Totally Looks Like - How Humans Compare, Compared to Machines 31

Totally Looks Like - How Humans Compare, Compared to Machine...

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

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

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations. Existing methods and datasets attempting to explain perceived similarity use stimuli which arguably do not cover the full breadth of factors that affect human similarity judgments, even those geared toward this goal. We introduce a new dataset dubbed Totally-Looks-Like (TLL) after a popular entertainment website, which contains images paired by humans as being visually similar. The dataset contains 6016 image-pairs from the wild, shedding light upon a rich and diverse set of criteria employed by human beings. We conduct experiments to try to reproduce the pairings via features extracted from state-of-the-art deep convolutional neural networks, as well as additional human experiments to verify the consistency of the collected data. Even though we create conditions to artificially make the matching task increasingly easier, we show that machine-extracted representations perform very poorly in terms of reproducing the matching selected by humans. The results suggest future directions for improvement of learned image representations. Data and code will be available at https://***/view/totally-looks-like-dataset.

关键词： Task analysis Feature extraction computer vision pattern recognition conferences Distance measurement Entertainment industry

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On the Suitability of L_p-norms for Creating and Preventing Adversarial Examples 31

On the Suitability of <i>L<sub>p</sub></i>-norms for Creatin...

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

作者： Sharif, Mahmood Bauer, Lujo Reiter, Michael K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Much research has been devoted to better understanding adversarial examples, which are specially crafted inputs to machine-learning models that are perceptually similar to benign inputs, but are classified differently (i.e., misclassified). Both algorithms that create adversarial examples and strategies for defending against adversarial examples typically use L-p-norms to measure the perceptual similarity between an adversarial input and its benign original. Prior work has already shown, however, that two images need not be close to each other as measured by an L-p-norm to be perceptually similar. In this work, we show that nearness according to an L-p-norm is not just unnecessary for perceptual similarity, but is also insufficient. Specifically, focusing on datasets (CIFAR10 and MNIST), L-p-norms, and thresholds used in prior work, we show through online user studies that "adversarial examples" that are closer to their benign counterparts than required by commonly used L-p-norm thresholds can nevertheless be perceptually distinct to humans from the corresponding benign examples. Namely, the perceptual distance between two images that are "near" each other according to an L-p-norm can be high enough that participants frequently classify the two images as representing different objects or digits. Combined with prior work, we thus demonstrate that nearness of inputs as measured by L-p-norms is neither necessary nor sufficient for perceptual similarity, which has implications for both creating and defending against adversarial examples. We propose and discuss alternative similarity metrics to stimulate future research in the area.

关键词： Machine learning Measurement Training computer vision Cancer Object recognition Face recognition

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Gait recognition by Deformable Registration 31

Gait Recognition by Deformable Registration

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

作者： Makihara, Yasushi Adachi, Daisuke Xu, Chi Yagi, Yasushi Osaka Univ Suita Osaka Japan Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper describes a method of gait recognition robust against intra-subject posture changes. A person sometimes walks with changing his/her posture when looking down at a smartphone or carrying a heavy object, which makes intra-subject variation large and consequently makes gait recognition difficult. We therefore introduce a deformable registration model to mitigate the intra-subject posture changes. More specifically, we represent a deformation field by a set of deformation vectors on lattice-type control points allocated on an image, i.e., by free-form deformation (FFD) framework. Given a pair of a probe and a gallery, we compute the deformation field so as to minimize the difference between a probe morphed by the deformation field and the gallery, as well as to ensure the spatial smoothness of the deformation field. We then learn the intra-subject eigen deformation modes from a training set of the same subjects' pairs (e.g., bending the upper body forward and swinging arms more), which are relatively different from inter-subject deformation modes (e.g., body shape spread and stride change). Moreover, because the deformable registration is responsible for a preprocessing part before matching, it can be combined with any types of matching algorithms for gait recognition. Experiments with 1,334 subjects show that the proposed method improves the gait recognition accuracy in both cases without and with a state-of-the-art deep learning-based matcher, respectively.

关键词： Strain Gait recognition Probes Deformable models Measurement Shape Computational modeling

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Dual-Mode Vehicle Motion pattern Learning for High Performance Road Traffic Anomaly Detection 31

Dual-Mode Vehicle Motion Pattern Learning for High Performan...

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

作者： Xu, Yan Ouyang, Xi Cheng, Yu Yu, Shining Xiong, Lin Ng, Choon-Ching Pranata, Sugiri Shen, Shengmei Xing, Junliang Panasonic R&D Ctr Singapore Singapore Singapore Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Nanyang Technol Univ Singapore Singapore Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Anomaly detection on road traffic is an important task due to its great potential in urban traffic management and road safety. It is also a very challenging task since the abnormal event happens very rarely and exhibits different behaviors. In this work, we present a model to detect anomaly in road traffic by learning from the vehicle motion patterns in two distinctive yet correlated modes, i.e., the static mode and the dynamic mode, of the vehicles. The static mode analysis of the vehicles is learned from the background modeling followed by vehicle detection procedure to find the abnormal vehicles that keep still on the road. The dynamic mode analysis of the vehicles is learned from detected and tracked vehicle trajectories to find the abnormal trajectory which is aberrant from the dominant motion patterns. The results from the dual-mode analyses are finally fused together by driven a re-identification model to obtain the final anomaly. Experimental results on the Track 2 testing set of NVIDIA AI CITY CHALLENGE show the effectiveness of the proposed dual-mode learning model and its robustness in different real scenes. Our result ranks the first place on the final Leaderboard of the Track 2.

关键词： Roads Automobiles Anomaly detection Vehicle dynamics Analytical models Tracking Surveillance

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

ISBN: (纸本)9781467388511

The proceedings contain 626 papers. The topics discussed include: learning to match aerial images with deep attentive architectures; learnt quasi-transitive similarity for retrieval from large collections of faces; unsupervised learning from narrated instruction videos; action recognition in video using sparse coding and relative features; recognizing emotions from abstract paintings using non-linear matrix completion; rolling shutter absolute pose problem with known vertical direction; memory efficient max flow for multi-label submodular MRFs; recovering the missing link: predicting class-attribute associations for unsupervised zero-shot learning; neural module networks; multi-cue zero-shot learning with strong supervision; social LSTM: human trajectory prediction in crowded spaces; NetVLAD: CNN architecture for weakly supervised place recognition; 3D semantic parsing of large-scale indoor spaces; efficient indexing of billion-scale datasets of deep descriptors; analyzing classifiers: Fisher vectors and deep neural networks; and GIFT: a real-time and scalable 3d shape search engine.

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On the iterative refinement of densely connected representation levels for semantic segmentation 31

On the iterative refinement of densely connected representat...

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

作者： Casanova, Arantxa Cucurull, Guillem Drozdzal, Michal Romero, Adriana Bengio, Yoshua Montreal Inst Learning Algorithms Montreal PQ Canada Comp Vis Ctr Barcelona Spain Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.

关键词： Semantics Spatial resolution computer architecture Convolutional codes Computational modeling computer vision Image segmentation

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