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Clothing Change Aware Person Identification 31

Clothing Change Aware Person Identification

引用

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

作者： Xue, Jia Meng, Zibo Katipally, Karthik Wang, Haibo van Zon, Kees Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ 08901 USA Univ South Carolina Dept Comp Sci & Engn Columbia SC USA Philips Res North Amer Cambridge MA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We develop a person identification approach - Clothing Change Aware Network (CCAN) for the task of clothing assisted person identification. CCAN concerns approaches that go beyond face recognition and particularly tackles the role of clothing to identification. Person identification is a rather challenging task when clothing appears changed under complex background information. With a pair of two person images as input, CCAN simultaneously performs a verification task to detect change in clothing and an identification task to predict person identity. When clothing from the pair of input images is detected to be different, CCAN automatically understates clothing information while emphasizing face, and vice versa. In practice, CCAN outperforms the way of equally stacking face and full body context features, and shows leading results on the People in Photo Album (PIPA) dataset.

关键词： Clothing Face Task analysis Face recognition Training Cameras

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Finding your Lookalike: Measuring Face Similarity Rather than Face Identity 31

Finding your Lookalike: Measuring Face Similarity Rather tha...

引用

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

作者： Sadovnik, Amir Gharbi, Wassim Thanh Vu Gallagher, Andrew Lafayette Coll Easton PA 18042 USA Google Res Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Face images are one of the main areas of focus for computer vision, receiving on a wide variety of tasks. Although face recognition is probably the most widely researched, many other tasks such as kinship detection, facial expression classification and facial aging have been examined. In this work we propose the new, subjective task of quantifying perceived face similarity between a pair of faces. That is, we predict the perceived similarity between facial images, given that they are not of the same person. Although this task is clearly correlated with face recognition, it is different and therefore justifies a separate investigation. Humans often remark that two persons look alike, even in cases where the persons are not actually confused with one another. In addition, because face similarity is different than traditional image similarity, there are challenges in data collection and labeling, and dealing with diverging subjective opinions between human labelers. We present evidence that finding facial look-alikes and recognizing faces are two distinct tasks. We propose a new dataset for facial similarity and introduce the Lookalike network, directed towards similar face classification, which outperforms the ad hoc usage of a face recognition network directed at the same task.

关键词： Face Face recognition Task analysis Training Feature extraction computer vision Neural networks

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Geometry-aware Traffic Flow Analysis by Detection and Tracking 31

Geometry-aware Traffic Flow Analysis by Detection and Tracki...

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

作者： Shi, Honghui Wang, Zhonghao Zhang, Yang Wang, Xinchao Huang, Thomas UIUC IFP Grp Beckman Inst Champaign IL 61820 USA IBM Res San Jose CA 95120 USA Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In the second Nvidia AI City Challenge hosted in 2018, the traffic flow analysis challenge proposes an interest task that requires participants to predict the speed of vehicles on road from various traffic camera videos. We propose a simple yet effective method combing both learning based detection and geometric calibration based estimation. We use a learning based method to detect and track vehicles, and use a geometry based camera calibration method to calculate the speed of those vehicles. We achieve a perfect detection rate of target vehicles and a root mean square error (RMSE) of 6.6674 in predicting the vehicle speed, which rank us the third place in the competition.

关键词： Videos Roads Cameras Estimation Tracking computer vision Artificial intelligence

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DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild 31

DisguiseNet : A Contrastive Approach for Disguised Face Veri...

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

作者： Peri, Skand Vishwanath Dhall, Abhinav Indian Inst Technol Ropar Learning Affect & Semant Image Anal LASII Grp Rupnagar Punjab India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper describes our approach for the Disguised Faces in theWild (DFW) 2018 challenge. The task here is to verify the identity of a person among disguised and impostors images. Given the importance of the task of face verification it is essential to compare methods across a common platform. Our approach is based on VGG-face architecture paired with Contrastive loss based on cosine distance metric. For augmenting the data set, we source more data from the internet. The experiments show the effectiveness of the approach on the DFW data. We show that adding extra data to the DFW dataset with noisy labels also helps in increasing the gen 11 eralization performance of the network. The proposed network achieves 27.13% absolute increase in accuracy over the DFW baseline.

关键词： Face Task analysis Face recognition Feature extraction Visualization Training Noise measurement

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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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Covariance Pooling for Facial Expression recognition 31

Covariance Pooling for Facial Expression Recognition

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

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

引用

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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Detection of Distracted Driver using Convolutional Neural Network 31

Detection of Distracted Driver using Convolutional Neural Ne...

引用

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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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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