3-D face recognition using curvelet local features

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    46 Citations (Scopus)


    In this letter, we present a robust single modality feature-based algorithm for 3-D face recognition. The proposed algorithm exploits Curvelet transform not only to detect salient points on the face but also to build multi-scale local surface descriptors that can capture highly distinctive rotation/displacement invariant local features around the detected keypoints. This approach is shown to provide robust and accurate recognition under varying illumination conditions and facial expressions. Using the well-known and challenging FRGC v2 dataset, we report a superior performance compared to other algorithms, with a 97.83% verification rate for probes with all facial expressions. © 2013 IEEE.
    Original languageEnglish
    Pages (from-to)172-175
    Number of pages4
    JournalIEEE Signal Processing Letters
    Issue number2
    Early online date13 Dec 2013
    Publication statusPublished - Feb 2014


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