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Dynamic Feature Matching for Partial Face Recognition.

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    This study introduces Dynamic Feature Matching (DFM), a novel approach for partial face recognition (PFR) using Fully Convolutional Networks and Sparse Representation Classification. DFM effectively recognizes faces from partial images without needing location data, improving surveillance and mobile device security.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Partial face recognition (PFR) is crucial for unconstrained environments like video surveillance and mobile devices, where occlusions or large viewing angles are common.
    • Existing PFR methods struggle with recognizing arbitrary face patches due to lack of attention and unsolved challenges.
    • The need for robust PFR systems is increasing with the proliferation of unconstrained visual data.

    Purpose of the Study:

    • To propose a novel and effective approach for partial face recognition (PFR) that addresses the challenges of unconstrained environments.
    • To develop a method that can recognize faces from arbitrary image patches without prior location information.
    • To improve the performance and efficiency of partial face recognition systems.

    Main Methods:

    • A novel approach named Dynamic Feature Matching (DFM) is proposed, combining Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC).
    • DFM processes the entire input image once to compute feature maps, enabling shared computation and significant speedup.
    • The method does not require prior position information of partial faces relative to a holistic face.

    Main Results:

    • Experimental results demonstrate the effectiveness of DFM on several partial face databases, including CAISA-NIR-Distance, CASIA-NIR-Mobile, and LFW.
    • DFM shows significant advantages compared to state-of-the-art PFR methods.
    • The approach also achieves impressive performance in partial person re-identification tasks on Partial RE-ID and iLIDS databases.

    Conclusions:

    • Dynamic Feature Matching (DFM) offers a robust and efficient solution for partial face recognition in unconstrained environments.
    • The proposed method overcomes limitations of existing PFR techniques by not requiring prior location information.
    • DFM shows strong potential for applications in surveillance, mobile biometrics, and person re-identification.