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Distributed eigenfaces for massive face image data

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Abstract

The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive face image collections. A parallel processing method using distributed eigenfaces is presented. A massive face image set was divided into a bunch of small subsets that satisfied the assumption of conventional approaches. Eigenfaces were extracted from the subsets and stored in a cloud system. Face recognition was performed by parallel processing using the distributed eigenfaces in the cloud system. A face recognition system was implemented in the Hadoop system. Various experiments were performed to test the validity of the distributed eigenface-based approach. The experimental results show that, compared to conventional methods, the implemented distributed face recognition system worked well for large datasets without significant performance degradation.

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Acknowledgements

This research was supported by the Chung-Ang University Excellent Student Scholarship and by Basic Science Research Programs through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2016R1D1A1B03936349 and NRF-2016R1D1A1B03933895).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

    Jeong-Keun Park & Jaehwa Park

  2. Department of Electronics and Electrical Engineering, Chung-Ang University, Seoul, Korea

    Ho-Hyun Park

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  1. Jeong-Keun Park
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  2. Ho-Hyun Park
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Correspondence to Jaehwa Park.

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Park, JK., Park, HH. & Park, J. Distributed eigenfaces for massive face image data. Multimed Tools Appl 76, 25983–26000 (2017). https://doi.org/10.1007/s11042-017-4823-6

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  • DOI: https://doi.org/10.1007/s11042-017-4823-6

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