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    Feature Transfer Learning for Deep Face Recognition with Long-Tail Data

    Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker

    Real-world face recognition datasets exhibit long-tail characteristics, which results in biased classifiers in conventionally-trained deep neural networks, or insufficient data when long-tail classes are ignored. In this paper, we propose to handle long-tail classes in the training of a face recognition engine by augmenting their feature space under a center-based ...

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    Keywords: Face Recognition

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    Face Anti-spoofing, Face Presentation Attack Detection

    Yaojie Liu, Amin Jourabloo, Yousef Atoum, Xiaoming Liu

    Biometrics utilize physiological, such as fingerprint, face, and iris, or behavioral characteristics, such as typing rhythm and gait, to uniquely identify or authenticate an individual. As biometric systems are widely used in real-world applications including mobile phone authentication and access control, biometric spoof, or Presentation Attack (PA) are becoming a ...

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    Keywords: Face antispoofing

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    Monocular Video-Based Trailer Coupler Detection using Multiplexer Convolutional Neural Network

    Yousef Atoum, Joseph Roth, Michael Bliss, Wende Zhang, Xiaoming Liu

    This paper presents a monocular camera-based computer vision system for autonomous selfbacking-up a vehicle towards a trailer, by continuously estimating the 3D trailer coupler position and feeding it to the vehicle control system, until the alignment of the tow hitch with the trailers coupler. This system is made possible through ...

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    Keywords: Application

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    Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

    Luan Tran, Xi Yin, Xiaoming Liu

    The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks ...

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    Keywords: Face Recognition, Face Reconstruction

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    Towards Large-Pose Face Frontalization in the Wild

    Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker

    Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments. Learning pose-invariant features is one solution, but needs expensively labeled large scale data and carefully designed feature learning algorithms. In this work, we focus on frontalizing faces in the ...

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    Keywords: Face Recognition, Face Reconstruction

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    Spatio-Temporal Alignment of Non-Overlapping Sequences from Independently Panning Cameras

    Seyed Morteza Safdarnejad, Xiaoming Liu

    We tackle a novel scenario of spatio-temporal alignment of seuqences referred to as Nonoverlapping Sequences (NOS). NOS are captured by multiple freely panning handheld cameras whose field of views (FOV) might have no direct spatial overlap. With the popularity of mobile sensors, NOS rise when multiple cooperative users capture a ...

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    Keywords: Sequence Alignment, Motion Compensation

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    Temporally Robust Global Motion Compensation by Keypoint-based Congealing

    Seyed Morteza Safdarnejad, Yousef Atoum, Xiaoming Liu

    Global motion compensation (GMC) removes the impact of camera motion and creates a video in which the background appears static over the progression of time. Various vision problems, such as human activity recognition, background reconstruction, and multi-object tracking can benefit from GMC. Existing GMC algorithms rely on sequentially processing consecutive ...

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    Keywords: Motion Compensation, Activity Recognition

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    Robust Global Motion Compensation in Presence of Predominant Foreground

    Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa

    Global motion compensation (GMC) removes intentional and unwanted camera motion. GMC is widely applicable for video stitching and, as a pre-processing module, for motion-based video analysis. While state-of-the-art GMC algorithms generally estimate homography satisfactorily between consecutive frames, their performances deteriorate on real-world unconstrained videos, for instance, videos with predominant foreground ...

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    Keywords: Motion Compensation, Activity Recognition

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    Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis

    Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus, John Wood, Dean Craven

    The amount of digital videos being created is increasing exponentially, e.g., YouTube has reached the upload rate of 100 hours of video per minute. A great deal of this growth is due to the tremendous popularity of smartphones and ubiquitous Internet access. This means that amateur-user generated videos form ...

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    Keywords: Database, Activity Recognition

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    Plant Vision

    Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer

    Plants are the major organisms that can absorb the light energy from the sun to produce biomass and oxygen. One key problem in studying plant growth is to understand the photosynthetic activities of plants under various external stimuli or genetic variations. Because leaves at different developmental ages may response to ...

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    Keywords: Plant vision

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    On developing and enhancing plant-level disease rating systems in real fields

    Yousef Atoum, Muhammad Jamal Afridi, Xiaoming Liu, J. Mitchell McGrath, Linda E. Hanson

    Cercospora leaf spot (CLS) is one of the most serious diseases of sugar beet worldwide, and if uncontrolled, causes nearly complete defoliation and loss of revenue for beet growers. The beet sugar industry continuously seeks CLS-resistant sugar beet cultivars as one strategy to combat this disease. Normally human experts manually ...

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    Keywords: Application

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    Image Segmentation of Mesenchymal Stem Cells in Diverse Culturing Conditions

    Muhammad Jamal Afridi, Chun Liu, Christina Chan, Seungik Baek, Xiaoming Liu

    Researchers in the areas of regenerative medicine and tissue engineering have an enormous interest in understanding the relationship of different sets of culturing conditions and applied mechanical stimuli on the behavior of Mesenchymal Stem Cells (MSCs). However, it remains a challenge to design a general tool to perform automatic cell ...

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