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Related Experiment Video

Updated: Jan 22, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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Cross-view Gait Recognition by Discriminative Feature Learning.

Yuqi Zhang, Yongzhen Huang, Shiqi Yu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2019
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    This summary is machine-generated.

    Researchers developed a novel Angle Center Loss (ACL) for cross-view gait recognition, improving feature distinctiveness. This method enhances intra-subject compactness and achieves state-of-the-art results on benchmark datasets.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Deep learning, particularly Convolutional Neural Networks (CNNs), is increasingly used for cross-view gait recognition.
    • Existing methods often employ loss functions from face recognition, which can suffer from hard negative mining issues.
    • There is a need for gait-specific loss functions that are robust and learn more discriminative features.

    Purpose of the Study:

    • To propose a novel, robust, and gait-related loss function called Angle Center Loss (ACL) for improved cross-view gait recognition.
    • To develop a method for extracting discriminative spatio-temporal gait features using local feature extractors and a temporal attention model.
    • To achieve state-of-the-art performance on cross-view gait recognition benchmarks.

    Main Methods:

    • Introduced Angle Center Loss (ACL), which learns multiple sub-centers per identity and penalizes the largest distance to improve intra-subject feature compactness.
    • Utilized a simplified spatial transformer network to identify and extract local gait features from relevant horizontal body parts.
    • Employed Long Short-Term Memory (LSTM) units as a temporal attention model to dynamically focus on discriminative frames, outperforming temporal average pooling and Gait Energy Images (GEI).

    Main Results:

    • The proposed ACL demonstrated robustness to variations in local body parts and temporal window sizes.
    • The combination of ACL, local feature extraction, and temporal attention significantly improved the discriminative power of gait features.
    • The integrated approach achieved state-of-the-art results on multiple cross-view gait recognition benchmark datasets.

    Conclusions:

    • The Angle Center Loss (ACL) is an effective and robust loss function for learning discriminative gait features in cross-view recognition.
    • The proposed spatio-temporal feature extraction method with temporal attention enhances the ability to recognize individuals from different viewpoints.
    • The study successfully advanced the performance of cross-view gait recognition systems, setting new benchmarks.