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Journal of Sensors
Volume 2016, Article ID 2575904, 21 pages
Research Article

Multithread Face Recognition in Cloud

1Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, Bardhaman, West Bengal 713205, India
2Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal 735102, India

Received 12 June 2016; Revised 13 September 2016; Accepted 12 October 2016

Academic Editor: Carlos Ruiz

Copyright © 2016 Dakshina Ranjan Kisku and Srinibas Rana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Faces are highly challenging and dynamic objects that are employed as biometrics evidence in identity verification. Recently, biometrics systems have proven to be an essential security tools, in which bulk matching of enrolled people and watch lists is performed every day. To facilitate this process, organizations with large computing facilities need to maintain these facilities. To minimize the burden of maintaining these costly facilities for enrollment and recognition, multinational companies can transfer this responsibility to third-party vendors who can maintain cloud computing infrastructures for recognition. In this paper, we showcase cloud computing-enabled face recognition, which utilizes PCA-characterized face instances and reduces the number of invariant SIFT points that are extracted from each face. To achieve high interclass and low intraclass variances, a set of six PCA-characterized face instances is computed on columns of each face image by varying the number of principal components. Extracted SIFT keypoints are fused using sum and max fusion rules. A novel cohort selection technique is applied to increase the total performance. The proposed protomodel is tested on BioID and FEI face databases, and the efficacy of the system is proven based on the obtained results. We also compare the proposed method with other well-known methods.