Matching 2.5D face scans to 3D models

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Matching 2.5D face scans to 3D models (2006)

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by Xiaoguang Lu , Anil K. Jain , Dirk Colbry

Venue:	PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON
Citations:	95 - 5 self

BibTeX

@ARTICLE{Lu06matching2.5d,
    author = {Xiaoguang Lu and Anil K. Jain and Dirk Colbry},
    title = {Matching 2.5D face scans to 3D models},
    journal = {PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON},
    year = {2006},
    volume = {28},
    number = {1},
    pages = {31--43}
}

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Abstract

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject’s pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.

Keyphrases

face scan face model face recognition system surface matching component face matching illumination variation matching scheme different modality recognition engine two-dimensional image three-dimensional shape information subject pose three-dimensional model weighted sum rule different pose independent test scan surface matching discriminant subspace analysis depth value appearance-based matching matching component different view arbitrary pose synthesized face image appearance-based matching stage candidate list modified iterative closest point appearance matching surface representation expression change new appearance sample facial scan experimental result

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