Pose-robust face signature for multi-view face recognition | IEEE Conference Publication | IEEE Xplore

Pose-robust face signature for multi-view face recognition


Abstract:

Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue. We propose a novel framewo...Show More

Abstract:

Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue. We propose a novel framework for multi-view face recognition based on extracting and matching pose-robust face signatures from 2D images. Specifically, we propose an efficient method for monocular 3D face reconstruction, which is used to lift the 2D facial appearance to a canonical texture space and estimate the self-occlusion. On the lifted facial texture we then extract various local features, which are further enhanced by the occlusion encodings computed on the self-occlusion mask, resulting in a pose-robust face signature, a novel feature representation of the original 2D facial image. Extensive experiments on two public datasets demonstrate that our method not only simplifies the matching of multi-view 2D facial images by circumventing the requirement for pose-adaptive classifiers, but also achieves superior performance.
Date of Conference: 08-11 September 2015
Date Added to IEEE Xplore: 17 December 2015
ISBN Information:
Conference Location: Arlington, VA, USA

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