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AnonymousNet: Natural Face De-Identification with Measurable Privacy 32

AnonymousNet: Natural Face De-Identification with Measurable...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Tao Lin, Lei Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Univ Rochester Goergen Inst Data Sci Rochester NY 14627 USA

ISBN: (纸本)9781728125060

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

关键词： computer vision

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Face recognition Algorithm Bias: Performance Differences on Images of Children and Adults 32

Face Recognition Algorithm Bias: Performance Differences on ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Srinivas, Nisha Ricanek, Karl Michalski, Dana Bolme, David S. King, Michael Univ North Carolina Wilmington Wilmington NC 28403 USA Def Sci & Technol Grp Edinburgh SA Australia Oak Ridge Natl Lab Oak Ridge TN USA Florida Inst Technol Melbourne FL 32901 USA

ISBN: (纸本)9781728125060

In this work, we examine if current state-of-the-art deep learning face recognition systems exhibit a negative bias (i.e., poorer performance) for children when compared to the performance obtained on adults. The systems selected for this work are five top performing commercial-off-the-shelf-face recognition systems, two government-off-the-shelf face recognition systems, and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on, over the last few years. Ourfindings show a negative bias for each algorithm on children. Genuine and imposter distributions highlight the performance bias between the datasets further supporting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score-level fusion strategies were evaluated. This work identifies the best score-level fusion technique for the child demographic.

关键词： Face recognition

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Cross-stream Selective Networks for Action recognition 32

Cross-stream Selective Networks for Action Recognition

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pan, Bowen Sun, Jiankai Lin, Wuwei Wang, Limin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728125060

Combining multiple information streams has shown obvious improvements in video action recognition. Most existing works handle each stream independently or perform a simple combination on temporally simultaneous samples in multi-streams, which fails to make full use of the stream wise complementary property due to the negligence of the temporal pattern gaps among streams. In this paper, we propose a cross-stream selective network (CSN) to properly integrate and evaluate information in multi-streams. The proposed CSN first introduces a local selective-sampling module (LSM), which can find asynchronous correspondences among streams and construct high-correlated sample groups across multiple information streams. This LSM can effectively deal with the temporal dis-alignment among different streams, leading to a better integration of cross-stream information. We further introduce a global adaptive weighting module (GAM). It adaptively evaluates the importance weights for each cross-stream sample group and selects temporally more important ones in action recognition. With the integration of cross-stream information, our GAM can obtain more reasonable importance than the existing single-stream weighting schemes. Extensive experiments on benchmark datasets of UCF101 and HMDB51 demonstrate the effectiveness of our approach over previous state-of-the-art methods.

关键词： computer vision

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Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification 32

Patch-based Discriminative Feature Learning for Unsupervised...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Qize Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Accuvis Technol Co Ltd Zhunan Town Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9781728132938

While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn discriminative feature from patches instead of the whole images. The patch-based learning leverages similarity between patches to learn a discriminative model. Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches. To provide effective guidance for the PatchNet to learn discriminative patch feature on unlabeled datasets, we propose an unsupervised patch-based discriminative feature learning loss. In addition, we design an image-level feature learning loss to leverage all the patch features of the same image to serve as an image-level guidance for the PatchNet. Extensive experiments validate the superiority of our method for unsupervised person re-id. Our code is available at https://***/QizeYang/PAUL.

关键词： Biometrics Categorization recognition: Detection Retrieval Video Analytics

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Multi-level 3D CNN for Learning Multi-scale Spatial Features 32

Multi-level 3D CNN for Learning Multi-scale Spatial Features

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ghadai, Sambit Lee, Xian Yeow Balu, Aditya Sarkar, Soumik Krishnamurthy, Adarsh Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9781728125060

3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and octrees) or from unstructured representations (graphs and point clouds). Learning features from such structured representations is limited by the restriction on resolution and tree depth while unstructured representations creates a challenge due to non uniformity among data samples. In this paper, we propose an end-to-end multi-level learning approach on a multi-level voxel grid to overcome these drawbacks. To demonstrate the utility of the proposed multi-level learning, we use a multi-level voxel representation of 3D objects to perform object recognition. The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object. In addition, each voxel in the coarse grid that contains a portion of the object boundary is subdivided into multiple fine-level voxel grids. The performance of our multi-level learning algorithm for object recognition is comparable to dense voxel representations while using significantly lower memory.

关键词： Object recognition

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Privacy-Preserving Action recognition using Coded Aperture Videos 32

Privacy-Preserving Action Recognition using Coded Aperture V...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zihao W. Vineet, Vibhav Pittaluga, Francesco Sinha, Sudipta N. Cossairt, Oliver Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Microsoft Res Redmond WA USA Univ Florida Gainesville FL 32611 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9781728125060

The risk of unauthorized remote access of streaming video from networked cameras underlines the need for stronger privacy safeguards. We propose a lens free coded aperture camera system for human action recognition that is privacy-preserving. While coded aperture systems exist, we believe ours is the first system designed for action recognition without the need for image restoration as an intermediate step. Action recognition is done using a deep network that takes in as input, non-invertible motion features between pairs offrames computed using phase correlation and log-polar transformation. Phase correlation encodes translation while the log polar transformation encodes in plane rotation and scaling. We show that the translation features are independent of the coded aperture design, as long as its spectral response within the bandwidth has no zeros. Stacking motion features computed on frames at multiple different strides in the video can improve accuracy. Preliminary results on simulated data based on a subset of the UCF and NTU datasets are promising. We also describe our prototype lens free coded aperture camera system, and results for real captured videos are mixed.

关键词： Image reconstruction

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Weakly Supervised Person Re-Identification 32

Weakly Supervised Person Re-Identification

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Meng, Jingke Wu, Sheng Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Accuvis Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728132938

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual;this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. Hence, for a video, there could be multiple video-level labels. We cast this weakly supervised person re-id challenge into a multi-instance multi-label learning (MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method that is able to explore potential intraclass person images from all the camera views by incorporating the intra-bag alignment and the cross-view bag alignment. Finally, the CV-MIML method is embedded into an existing deep neural network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have performed extensive experiments to show the feasibility of the proposed weakly supervised setting and verify the effectiveness of our method compared to related methods on four weakly labeled datasets.

关键词： And Body Pose Categorization Face Gesture recognition: Detection Retrieval Scene Analysis and Understanding

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FaceGenderID: Exploiting Gender Information in DCNNs Face recognition Systems 32

FaceGenderID: Exploiting Gender Information in DCNNs Face Re...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Vera-Rodriguez, Ruben Blazquez, Marta Morales, Aythami Gonzalez-Sosa, Ester Neves, Joao C. Proenca, Hugo Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain Nokia Bell Labs Madrid Spain Univ Beira Interior Inst Telecomun Covilha Portugal

ISBN: (纸本)9781728125060

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as liGG-Face or ResNet-50, achieve very high performance, they are (rained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent (raining approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally we announce the availability (at GitHubl) of the FaceGenderlD DCNNs models proposed in this work, which can support further experiments on (his topic.

关键词： Large dataset

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Masked Graph Attention Network for Person Re-identification 32

Masked Graph Attention Network for Person Re-identification

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bao, Liqiang Ma, Bingpeng Chang, Hong Chen, Xilin Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China

ISBN: (纸本)9781728125060

The mainstream methods for person re-identification (ReID) mainly focus on the correspondence between individual sample images and labels, while ignoring rich global mutual information resides in the whole sample set. We propose a method called Masked Graph Attention Network (MGAT) to address this problem. MGAT operates on the complete graph constructed with the extracted features, where nodes are able to directionally attend over other nodes' features under the guidance of label information in the form of mask matrix. By using MGAT module, the previously neglected global mutual information is exploited to generate an optimized feature space with more discriminant power. Meanwhile, we propose to feedback the optimization information learned by MGAT module to the feature-embedding network to enhance the mapping capability, thus avoiding the difficulty to handle large-scale graphs in testing phase. To evaluate our method, we conduct experiments on three commonly used ReID datasets. The results show that our method outperforms most mainstream methods, and is highly comparable to the state-of-the-art method.

关键词： computer vision

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Hierarchical Feature-Pair Relation Networks for Face recognition 32

Hierarchical Feature-Pair Relation Networks for Face Recogni...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kang, Bong-Nam Kim, Yonghyun Jun, Bongjin Kim, Daijin POSTECH Dept Comp Sci & Engn Pohang Si Gyeongsangbuk D South Korea Stradvision Inc Seoul South Korea

ISBN: (纸本)9781728125060

We propose a novel face recognition method using a Hierarchical Feature Relational Network (HFRN) which extracts facial part representations around facial landmark points, and predicts hierarchical latent relations between facial part representations. These hierarchical latent relations should be unique relations within the same identity and discriminative relations among different identities for face recognition task. To do this, the HFRN extracts appearance features as facial parts representations around facial landmark points on the feature maps, globally pool these extracted appearance features onto single feature vectors, and captures the relations for the pairs of appearance features. The HFRN captures the locally detailed relations in the low-level layers and the locally abstracted global relations in the high-level layers for the pairs of appearance features extracted around facial landmark points projected on each layer, respectively. These relations from low-level layers to high-level layers are concatenated into a single hierarchical relation feature. To further improve the accuracy offace recognition, we combine the global appearance feature with the hierarchical relation feature. In experiments, the proposed method achieves the comparable performance in the I :I face verification and I :N face identification tasks compared to existing state-of-the-art methods on the challenging IARPA Janus Benchmark A (IJB-A) and IARPA Janus Benchmark B (IJB-B) datasets.

关键词： Face recognition

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