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  • Recognizing Partially Occluded Faces from a Single Sample Per Class Using String-Based Matching

    Author(s)
    Chen, Weiping
    Gao, Yongsheng
    Griffith University Author(s)
    Chen, Luke
    Gao, Yongsheng
    Year published
    2010
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    Abstract
    Automatically recognizing human faces with partial occlusions is one of the most challenging problems in face analysis community. This paper presents a novel string-based face recognition approach to address the partial occlusion problem in face recognition. In this approach, a new face representation, Stringface, is constructed to integrate the relational organization of intermediate-level features (line segments) into a high-level global structure (string). The matching of two faces is done by matching two Stringfaces through a string-to-string matching scheme, which is able to efficiently find the most discriminative local ...
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    Automatically recognizing human faces with partial occlusions is one of the most challenging problems in face analysis community. This paper presents a novel string-based face recognition approach to address the partial occlusion problem in face recognition. In this approach, a new face representation, Stringface, is constructed to integrate the relational organization of intermediate-level features (line segments) into a high-level global structure (string). The matching of two faces is done by matching two Stringfaces through a string-to-string matching scheme, which is able to efficiently find the most discriminative local parts (substrings) for recognition without making any assumption on the distributions of the deformed facial regions. The proposed approach is compared against the state-of-the-art algorithms using both the AR database and FRGC (Face Recognition Grand Challenge) ver2.0 database. Very encouraging experimental results demonstrate, for the first time, the feasibility and effectiveness of a high-level syntactic method in face recognition, showing a new strategy for face representation and recognition.
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    Conference Title
    COMPUTER VISION-ECCV 2010, PT III
    Volume
    6313
    Issue
    PART 3
    DOI
    https://doi.org/10.1007/978-3-642-15558-1_36
    Publication URI
    http://hdl.handle.net/10072/36787
    Collection
    • Conference outputs

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