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Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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Anchor-based Plain Net for Mobile Image Super-Resolution

Anchor-based Plain Net for Mobile Image Super-Resolution

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Du, Zongcai Liu, Jie Tang, Jie Wu, Gangshan Nanjing Ubivers State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://***/NJUJet/SR_Mobile_Quantization.

关键词： Training computer vision Quantization (signal) conferences Computational modeling Superresolution Random access memory

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InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Hui-Po Orekondy, Tribhuvanesh Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781665448994

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains;(b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images and additionally increases uncertainty by 6.2x (or up to 0.85 bits) over the non-obfuscated counterparts.

关键词： Privacy Visualization Interpolation computer vision Uncertainty Target tracking conferences

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SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jo, Younghyun Yang, Sejong Kim, Seon Joo Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665448994

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact mapping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model parameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for x4 and x8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.

关键词： Couplings Training Manifolds computer vision conferences Superresolution Stacking

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Computationally Efficient Face Spoofing Detection with Motion Magnification

Computationally Efficient Face Spoofing Detection with Motio...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Bharadwaj, Samarth Dhamecha, Tejas I. Vatsa, Mayank Singh, Richa IIIT Delhi Delhi India

ISBN: (纸本)9780769549903

For a robust face biometric system, a reliable anti-spoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance;especially HOOF descriptor yielding a near perfect half total error rate of 0% and 1.25% respectively.

关键词： biometrics (access control) computer crime face recognition feature extraction image enhancement image texture motion estimation video databases video signal processing

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Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management

Multi-Target Multi-Camera Vehicle Tracking for City-Scale Tr...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shim, Kyujin Yoon, Sungjoon Ko, Kangwook Kim, Changick Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781665448994

Multi-target multi-camera (MTMC) tracking is one of the important fields in computer vision, where multiple objects are tracked across multiple cameras. MTMC tracking can be applied to various tasks such as crowd analysis, city-scale traffic management, and transportation systems analysis for intelligent city planning. However, it is challenging due to the large variety of conditions of each camera, such as perspective and illumination. Furthermore, MTMC tracking for vehicles is more problematic because of the relatively large inter-class similarity and intra-class variability. In this paper, we tackle the MTMC tracking problem for vehicles by dividing it into three main steps: (i) vehicle detection and feature extraction, (ii) multi-target single-camera tracking using the appearance feature of each vehicle, and (iii) multi-camera association of local trajectories from each camera. Our method shows comparable results with other highly-ranked methods in AI City Challenge 2021 and outperforms a recent MTMC tracking method that ranked first place in AI City Challenge 2020.

关键词： computer vision Urban planning Pipelines Transportation Cameras Feature extraction Prediction algorithms

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CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

CNN-based morphological decomposition of X-ray images for de...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Madmad, Tahani Delinte, Nicolas De Vleeschouwer, Christophe UCLouvain ICTEAM Louvain La Neuve Belgium

ISBN: (纸本)9781665448994

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.

关键词： computer vision Dictionaries Heuristic algorithms conferences Data visualization Dynamic range Approximation algorithms

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Isolated Sign recognition from RGB Video using Pose Flow and Self-Attention

Isolated Sign Recognition from RGB Video using Pose Flow and...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： De Coster, Mathieu Van Herreweghe, Mieke Dambre, Joni Univ Ghent IMEC IDLab AIRO Technol Pk Zwijnaarde 126 Ghent Belgium Univ Ghent Blandijnberg 2 Ghent Belgium

ISBN: (纸本)9781665448994

Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that require video understanding, e.g., action recognition. However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain. In this work, we reduce the impact of this data limitation by automatically pre-extracting useful information from the sign language videos. In our approach, different types of information are offered to a VTN in a multi-modal setup. It includes per-frame human pose keypoints (extracted by OpenPose) to capture the body movement and hand crops to capture the (evolution of) hand shapes. We evaluate our method on the recently released AUTSL dataset for isolated sign recognition and obtain 92.92% accuracy on the test set using only RGB data. For comparison: the VTN architecture without hand crops and pose flow achieved 82% accuracy. A qualitative inspection of our model hints at further potential of multi-modal multi-head attention in a sign language recognition context.

关键词： computer vision Analytical models Shape Assistive technology Computational modeling Gesture recognition computer architecture

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Empirical mode decomposition liveness check in fingerprint time series captures

Empirical mode decomposition liveness check in fingerprint t...

引用

2006 conference on computer vision and pattern recognition workshops

作者： Abhyankar, Aditya Schuckers, Stephanie Electrical and Computer Engineering Department Clarkson University Potsdam NY 13699 United States

ISBN: (纸本)0769526462

This work demonstrates a faster approach for liveness detection in fingerprint devices. The physiological phenomenon of perspiration, observed in time-series finger-print images of live people, is used as a measure to classify 'live' fingers from 'not live' fingers. Pre-processing involves finding the singularity points using wavelets in the fingerprint images and transforming the information back in the spatial domain to form a spatial domain signal. Wavelet packet sieving is used to tune the modes so as to gain physical significance with reference to the evolving perspiration pattern in 'live' fingers. The percentage of energy contribution in the difference modes is used as a measure to differentiate live fingers from others. The proposed algorithm was applied to a data set of approximately 58 live, 50 spoof and 28 cadaver fingerprint images captured at 0 and after 2 sec, from three different types of scanners. An overall classification rate of 93.7% was achieved across all the three scanners. © 2006 IEEE.

关键词： pattern recognition

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Unsupervised Anomaly Detection from Time-of-Flight Depth Images

Unsupervised Anomaly Detection from Time-of-Flight Depth Ima...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Schneider, Pascal Rambach, Jason Mirbach, Bruno Stricker, Didier German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.

关键词： Training Optical losses computer vision Cameras Transformers Sensors Task analysis

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