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A novel framework for automatic 3D face recognition using quality assessment

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Abstract

The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance. In this paper, we present a novel 3D face recognition system which performs quality assessment on input images prior to recognition. More specifically, a reject option is provided to allow the system operator to eliminate the incoming images of poor quality, e.g. failure acquisition of 3D image, exaggerated facial expressions, etc.. Furthermore, an automated approach for preprocessing is presented to reduce the number of failure cases in that stage. The experimental results show that the 3D face recognition performance is significantly improved by taking the quality of 3D facial images into account. The proposed system achieves the verification rate of 97.09% at the False Acceptance Rate (FAR) of 0.1% on the FRGC v2.0 data set.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

    Wei-Yang Lin & Ming-Yang Chen

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  1. Wei-Yang Lin
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  2. Ming-Yang Chen
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Correspondence to Wei-Yang Lin.

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Lin, WY., Chen, MY. A novel framework for automatic 3D face recognition using quality assessment. Multimed Tools Appl 68, 877–893 (2014). https://doi.org/10.1007/s11042-012-1092-2

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