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Related Concept Videos

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Deconvolution01:20

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Related Experiment Video

Updated: Mar 3, 2026

Automatic Identification of Dendritic Branches and their Orientation
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Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

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Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition.

Changxing Ding, Dacheng Tao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 6, 2017
    PubMed
    Summary

    This study introduces a robust Convolutional Neural Network (CNN) framework for video-based face recognition (VFR). The approach enhances blur-insensitive features and pose/occlusion robustness, achieving state-of-the-art results on benchmark datasets.

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    Last Updated: Mar 3, 2026

    Automatic Identification of Dendritic Branches and their Orientation
    06:08

    Automatic Identification of Dendritic Branches and their Orientation

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Biometrics

    Background:

    • Human face recognition in surveillance videos faces significant challenges including image blur, pose variations, and occlusion.
    • Existing methods often struggle with the quality degradation and variability present in real-world video data.

    Purpose of the Study:

    • To develop a comprehensive framework using Convolutional Neural Networks (CNNs) for robust video-based face recognition (VFR).
    • To enhance the model's ability to handle blur, pose variations, and occlusion in facial images.

    Main Methods:

    • Artificially blurring clear still images to create blur-robust training data for CNNs.
    • Proposing a Trunk-Branch Ensemble CNN (TBE-CNN) to extract complementary features from holistic faces and facial patches.
    • Implementing an improved triplet loss function to enhance feature discriminative power.

    Main Results:

    • The proposed CNN framework effectively learns blur-insensitive features.
    • TBE-CNN demonstrates enhanced robustness against pose variations and occlusion.
    • Achieved state-of-the-art performance on PaSC, COX Face, and YouTube Faces video face databases.
    • Secured first place in the BTAS 2016 Video Person Recognition Evaluation.

    Conclusions:

    • The developed framework significantly improves video-based face recognition accuracy.
    • The combination of data augmentation, ensemble architecture, and improved loss function leads to superior performance.
    • The proposed methods represent a substantial advancement in handling challenging real-world surveillance video data.