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KINECT Face Recognition Using Occluded Area Localization Method

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Transactions on Computational Science XXX

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 10560))

Abstract

Automated face recognition is commonly used for security reinforcement and identity verification purposes. While significant advancement has been made in this domain, modern surveillance techniques are still dependent on variations in pose, orientation of the facial images, difference in the illumination, occlusion, etc. Therefore, face recognition or identification in uncontrolled situations has become an important research topic. In this paper, we propose a new face recognition technique that takes into account partial occlusion, while still accurately identifying the user. The occluded facial areas are detected from the Kinect depth images by extracting features using Uniform Local Binary Pattern (LBP). For localizing occluded regions from the Kinect depth images, a threshold based approach is used to identify the areas close to the camera. The recognition system will discard the occluded regions of the facial images and match only the non-occluded facial part with the gallery of images to find the best possible match. The performance of the recognition system has been evaluated on EUROKOM Kinect face database containing different types of occluded and non-occluded faces with neutral expressions. Experimental results show that the proposed method improves the recognition rate by 4.8% and 5.7% for occlusion by hand and occlusion by paper, respectively.

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Acknowledgments

We would like to acknowledge NSERC Discovery Grant RT731064, as well as NSERC ENGAGE and URGC for partial funding of this project. Our thanks to all the members of BTLab, Department of Computer Science, University of Calgary, Calgary, AB, Canada for providing their valuable suggestions and feedback.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada

    Fatema Tuz Zohra & Marina Gavrilova

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  1. Fatema Tuz Zohra
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  2. Marina Gavrilova
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Correspondence to Fatema Tuz Zohra .

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Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems, Tallinn, Estonia

    C.J. Kenneth Tan

  3. Nanyang Technological University, Singapore, Singapore

    Alexei Sourin

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Zohra, F.T., Gavrilova, M. (2017). KINECT Face Recognition Using Occluded Area Localization Method. In: L. Gavrilova, M., Tan, C., Sourin, A. (eds) Transactions on Computational Science XXX. Lecture Notes in Computer Science(), vol 10560. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56006-8_2

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  • DOI: https://doi.org/10.1007/978-3-662-56006-8_2

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