Distributed eigenfaces for massive face image data
- Authors
- Park, Jeong-Keun; Park, Ho-Hyun; Park, Jaehwa
- Issue Date
- Dec-2017
- Publisher
- SPRINGER
- Keywords
- Eigenface; Face recognition; Parallel processing; Hadoop
- Citation
- MULTIMEDIA TOOLS AND APPLICATIONS, v.76, no.24, pp 25983 - 26000
- Pages
- 18
- Journal Title
- MULTIMEDIA TOOLS AND APPLICATIONS
- Volume
- 76
- Number
- 24
- Start Page
- 25983
- End Page
- 26000
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3559
- DOI
- 10.1007/s11042-017-4823-6
- ISSN
- 1380-7501
1573-7721
- 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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- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
- College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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