Distributed eigenfaces for massive face image data
| Autor: | Jeong-Keun Park, Ho-Hyun Park, Jaehwa Park |
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| Rok vydání: | 2017 |
| Předmět: |
Pixel
Computer Networks and Communications Computer science business.industry Pattern recognition 02 engineering and technology Facial recognition system Image (mathematics) Set (abstract data type) Parallel processing (DSP implementation) Eigenface Hardware and Architecture 020204 information systems Face (geometry) 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Software |
| Zdroj: | Multimedia Tools and Applications. 76:25983-26000 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-017-4823-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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